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Course Name : Creating Problem-Solving Agents using GenAI for Action Composition || Course Description : Creating Problem-Solving Agents using GenAI for Action Composition
Discover how Generative AI is revolutionizing Agentic AI systems to solve complex real-world problems.
Enroll for free
Course Description
This introductory course provides a concise overview of Agentic AI systems, covering their evolution, current state, and practical applications. You will explore key topics including the history of Agentic AI systems, the role of agents today, multi-agent systems, and practical solutions for implementing them. Perfect for those seeking a foundational understanding of intelligent Agentic AI systems in action.
Course curriculum
1
Creating Problem-Solving Agents using GenAI for Action Composition
Introduction
Overview- Count the Number of Agents
A brief history of Agentic Systems
Agents Today
Multi-Agent Systems Today
Practical Solutions
Who should Enroll?
Beginners in AI and ML looking to understand agentic systems and their real-world applications.
Tech enthusiasts and developers interested in learning the basics of creating intelligent, problem-solving agents.
Professionals exploring multi-agent systems for automation and dynamic task orchestration in various domains.
Key Takeaways
Understand the evolution and current capabilities of agentic systems.
Learn the basics of agents and multi-agent systems, including their role in solving complex problems.
Gain insights into practical applications of agents and how they function in real-world scenarios.
About the Instructor
Vikas Agrawal - Senior Principal Data Scientist at Oracle Analytics Cloud
Vikas Agrawal is a Senior Principal Data Scientist at Oracle Analytics Cloud, focused on designing and deploying AI solutions across ERP, SCM, HCM, CX, and MFG for Fusion and NetSuite customers. He ensures that AI models are automatically generated, updated, and tailored to each customer's evolving data. His research involves developing intelligent agents that leverage domain knowledge to solve complex problems by combining tools and critiqued LLMs/LMMs for hypothesis generation and task orchestration. An electrical engineer turned computer scientist, Vikas is an IIT Delhi graduate with experience at CalTech, Intel, and Infosys.
Frequently Asked Questions (FAQs)
What is an agentic system?
An agentic system refers to an AI-based structure where agents can autonomously perform tasks, often in collaboration, to solve complex problems.
Do I need prior AI knowledge to take this course?
No, this course is beginner-friendly and provides an introduction to agentic systems and their applications.
Are there any hands-on projects in this course?
Yes, you will see practical demos that illustrate how agents are created and used in real-world applications.
How will this course benefit me professionally?
You'll gain foundational knowledge of AI agents, enabling you to apply these concepts to automation, AI development, and other tech projects involving intelligent systems.
Course Name : Improving Real World RAG Systems: Key Challenges & Practical Solutions || Course Description : Improving Real World RAG Systems: Key Challenges & Practical Solutions
Master key challenges in real-world Retrieval-Augmented Generation (RAG) systems. Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
Enroll for free
Course Description
This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.
Course curriculum
1
Improving Real World RAG System
Introduction to RAG Systems
Resources
RAG System Challenges Practical Solutions
Hands-on: Solution for Missing Content in RAG
Other Key Challenges
Practical Solutions
Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed
Wrong Format Problem Solution
Hands-on: Solution for Wrong Format
Incomplete Problem Solution
HyDE
Other Practical Solutions from recent Research Papers
Who should Enroll?
AI/ML professionals aiming to enhance RAG system performance and solve real-world challenges.
Developers/Engineers building search, conversational, or generative AI systems needing better data retrieval and context handling.
Researchers/Enthusiasts seeking hands-on experience with advanced RAG techniques and agentic systems.
Key Takeaways
Master RAG systems with a solid grasp of architecture and components.
Solve key challenges like missing content and hallucinations.
Optimize performance with advanced chunking and retrieval strategies.
Develop practical decision-making skills for LLM adoption in various industries.
About the Instructor
Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya
Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
Frequently Asked Questions (FAQs)
What prior knowledge is required?
A basic understanding of AI/ML principles is needed, along with some experience in machine learning frameworks such as PyTorch or TensorFlow. Familiarity with natural language processing (NLP) concepts will be helpful but not mandatory.
Will there be hands-on exercises?
Yes, the course provides practical, hands-on exercises through demos and notebooks. You’ll have opportunities to implement RAG systems and experiment with real-world use cases, focusing on improving retrieval and generation tasks.
What tools or software will I need?
You’ll need access to Python, Jupyter Notebooks, and relevant libraries such as LangChain, Hugging Face, and vector databases. The course will guide you through setting up the necessary environment for practicing the techniques.
How is this course different from other AI courses?
Unlike general AI/ML courses, this course zeroes in on Retrieval-Augmented Generation (RAG) systems, addressing practical challenges like hallucinations, retrieval errors, and context optimization, with a strong emphasis on real-world applications.
Will I learn advanced techniques?
Yes, the course covers advanced techniques like hyperparameter tuning, chunking strategies, embedding models, context compression, and agentic RAG systems, giving you the tools to build and optimize high-performing RAG systems.
Course Name : Framework to Choose the Right LLM for your Business || Course Description : Framework to Choose the Right LLM for your Business
This course provides a comprehensive framework for selecting the right LLM for your business. Learn to evaluate LLMs based on accuracy, cost, scalability, and more, while exploring real-world applications to make informed, strategic AI decisions.
Enroll for free
Course Description
This course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively.
Course curriculum
1
Introduction
Introduction
2
It's an LLM World!
3
Understand Your Business
4
Framework to Choose the Right LLM
5
Case Studies
6
Conclusion
Who should Enroll?
Business leaders seeking to implement AI-driven solutions efficiently.
Data scientists exploring LLMs for industry-specific applications.
Tech professionals involved in AI integration and decision-making processes.
Key Takeaways
Understand how to evaluate and select the right LLM for business needs.
Learn to assess LLMs based on accuracy, cost, scalability, and integration.
Gain insights into real-world LLM applications through case studies.
Develop practical decision-making skills for LLM adoption in various industries.
About the Instructor
Rohan Rao - Principal Data Scientist, H2O.ai; Quadruple Kaggle Grandmaster
A Principal Data Scientist at H2O.ai and an IIT-Bombay alumnus, is a highly accomplished professional. He is a quadruple Kaggle Grandmaster and was formerly ranked #1 on AnalyticsVidhya. In addition to his expertise in data science, Rohan is a 9-time National Sudoku Champion. A versatile individual, he is also a passionate coder, reader, writer, and lifelong learner, known in the community as "vopani."
FAQ
What factors should be considered when choosing an LLM for business?
Key factors include the LLM's accuracy, cost, scalability, technical compatibility, support, security, and compliance with privacy laws. The decision-making framework ensures the chosen LLM aligns with specific business requirements.
Can any LLM solve all business problems?
No, different LLMs are suited to different tasks. Selecting the right LLM depends on the specific business problem, required capabilities, and available resources.
How important is the accuracy of an LLM for business use?
Accuracy is crucial, especially in fields like healthcare and education. LLMs must perform reliably across datasets, ensuring consistency and stability in results for critical business applications.
What are the key decisions for using LLMs in healthcare?
Key decisions include choosing an LLM fine-tuned for medical data, ensuring accuracy, maintaining privacy, and complying with healthcare regulations.
Are open-source LLMs viable alternatives to closed-source options?
Yes, open-source LLMs, like Llama3, can be viable alternatives, especially when customized to specific business needs. They are catching up with closed-source options in performance.
Course Name : Building Smarter LLMs with Mamba and State Space Model || Course Description : Building Smarter LLMs with Mamba and State Space Model
Master Mamba's selective state space model for LLMs. Discover key components like the Mamba block, optimizing sequence modeling with efficient, scalable training and inference, surpassing traditional Transformers.
Enroll for free
Course Description
Unlock the Power of State Space Models (SSM) like Mamba with our comprehensive course designed for AI professionals, data scientists, and NLP enthusiasts. Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures.
Course curriculum
1
Course Overview
Course Overview
2
An Alternative to Transformers
3
Understanding State Space Models
4
Mamba - A Selective State Space Model
Who should Enroll?
AI and ML professionals looking to specialize in State Space Models and Mamba architecture.
Data scientists interested in exploring advanced Generative AI models and architectures.
NLP practitioners who want to integrate SSMs like Mamba in their workflows and use cases.
Key Takeaways
A comprehensive understanding of State Space Models (SSM)
In-depth exploration of The Mamba Architecture
Visual guides and workflows on SSM and Mamba
Advanced applications, comparisons and practical use cases.
About the Instructor
Maarten Grootendorst - Senior Clinical Data Scientist, IKNL; Creator of KeyBERT and BERTopic
Marteen holds three master’s degrees in Organizational, Clinical Psychology, and Data Science, using them to simplify machine learning for a broad audience. As co-author of Hands-On Large Language Models and through popular blogs, he’s reached millions by explaining AI, often from a psychological lens. He’s also the creator of widely-used open-source packages like BERTopic, PolyFuzz, and KeyBERT, which have millions of downloads and are utilized by data professionals globally.
FAQ
What are State Space Models (SSM) in machine learning?
State Space Models (SSM) are used in machine learning to model and predict systems that evolve over time. They represent the system's state as a dynamic process, helping to capture temporal patterns in data, making them useful for tasks like time series forecasting, control systems, and natural language processing.
How do State Space Models differ from traditional RNNs?
State Space Models (SSM) and traditional Recurrent Neural Networks (RNNs) both handle sequential data, but they differ in approach. SSMs use a mathematical framework to model the system's state and evolution over time explicitly. In contrast, RNNs use neural networks to implicitly learn patterns in sequences without explicitly modeling the system's state.
What is the Mamba architecture in AI?
Mamba is an alternative AI architecture designed to address the limitations of traditional transformers. It enhances efficiency with optimizations like RMSnorm and offers significant improvements in inference speed—up to 5× higher throughput. Mamba also scales linearly with sequence length, making it highly effective for handling real-world data, even with sequences up to a million tokens. As a versatile backbone, Mamba achieves state-of-the-art performance across various domains, including language, audio, and genomics. Notably, the Mamba-3B model outperforms transformers of the same size and rivals those twice its size in both pretraining and downstream evaluation.
How does Mamba compare to transformer models?
Mamba architecture differs from traditional transformer models by leveraging state-space models (SSMs) instead of the self-attention mechanism. This key difference allows Mamba to achieve linear complexity scaling with sequence length, a significant improvement over the quadratic scaling seen in transformers. While transformers excel in parallel processing with self-attention, Mamba's use of SSMs enables it to handle sequences more efficiently, especially in tasks involving long sequences, while still supporting parallel processing during training.
What are the applications of State Space Models in NLP?
State Space Models (SSM) are used in NLP for similar applications as other Language Models (LLMs), such as predicting and modeling sequential language patterns. However, SSMs stand out due to their ability to handle long text sequences more efficiently, making them particularly advantageous in tasks that involve processing extensive dependencies within the text.
How do transformers work in large language models?
Transformers use self-attention mechanisms to process input data in parallel, allowing large language models to efficiently learn relationships between words in a sequence, improving performance in tasks like translation and text generation.
Why are RNNs considered in AI despite transformer popularity?
RNNs are still used in AI because they excel at handling sequential data with strong temporal dependencies, and their simpler architecture can be advantageous in specific applications where transformers might be overkill.
What are the benefits of using Mamba in deep learning?
Mamba architecture offers improved efficiency in deep learning models, particularly in handling complex tasks and large-scale AI applications, making it a powerful alternative to traditional transformers.
How do State Space Models improve the accuracy of AI models, such as RNNs or Transformers in processing temporal data?
State Space Models improve AI accuracy by explicitly modeling the underlying state of a system over time, leading to better predictions and more interpretable results, especially in time-sensitive applications.
Course Name : Generative AI - A Way of Life - Free Course || Course Description : Generative AI - A Way of Life - Free Course
Embark on a journey into Generative AI for beginners. Learn AI-powered text and image generation, use top AI tools, and explore industry applications. Gain practical skills, understand ethical practices, and master prompting techniques.
Enroll for free
6 Hours
4.7/5
Beginner
Generative AI - A Way of Life
This course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL·E3. Learn practical applications across industries, ethical considerations, and best practices. Whether you're a content creator, business innovator, or AI enthusiast, gain the expertise to harness Generative AI's full potential and drive innovation in your field.
Course curriculum
1
Introduction to Generative AI
Fundamentals of Generative AI
What is Generative AI?
How does Generative AI work?
Exploring the Potential of Generative AI
GenAI Pinnacle Program
Hands On: Let’s get generating!
2
Text Generation Using Generative AI
3
Image Generation Using Generative AI
Enroll for Free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Who Should Enroll:
This course is perfect for beginners with no technical background, professionals looking to enhance their AI skills, students eager to explore AI, and content creators seeking to leverage AI tools. If you're curious about AI's potential and want to stay ahead in your field, this course is for you.
Instructor
Apoorv Vishnoi, Head - Training Vertical, Analytics Vidhya
Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.
Common Questions Beginners Ask About Generative AI
What is Generative AI?
Generative AI refers to algorithms that can create new data, such as images, text, and music, by learning patterns from existing data.
Do I need to know programming to learn Generative AI?
Having a basic understanding of Python programming is not compulsory, but if you are interested in learning Python there's also a free course available on our platform.
What are the real-world applications of Generative AI?
From AI art to content creation tools and personalized marketing campaigns, Generative AI is revolutionizing industries worldwide.
How does Generative AI differ from traditional AI?
Traditional AI focuses on classification and prediction, while Generative AI creates new content or data based on learned patterns.
Is this course suitable for beginners?
Absolutely! This course is designed for all levels, including those new to machine learning and deep learning.

Key Takeaways from this Course
Utilize leading AI tools like ChatGPT, Microsoft Copilot, and DALL·E3 to create text and image content, enhancing your ability to innovate and streamline your creative processes.
Explore practical use cases across various industries, from marketing to finance, and apply ethical best practices, ensuring you are well-equipped to implement AI solutions responsibly and effectively.
Course Name : Building LLM Applications using Prompt Engineering - Free Course || Course Description : Building LLM Applications using Prompt Engineering - Free Course
This free course offers a comprehensive guide on building LLM applications, mastering prompt engineering, implementing best practices, and developing chatbots using enterprise data using advanced techniques like few-shot or one-shot prompting
Enroll for free
2.1 Hours
4.6/5
Beginner
Who Should Enroll?
This course will provide you with a hands-on understanding of building LLM applications and mastering prompt engineering techniques. By the end of the course, you will be proficient in implementing and fine-tuning these techniques to enhance generative AI model performance. You'll learn to apply various prompting methods and build chatbots on enterprise data, equipping you with the skills to improve conversational AI systems in real-world projects.
Who Should Enroll:
Professionals: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.
Aspiring Students: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.

Course curriculum
1
How to build diffferent LLM AppIications?
Introduction to Building Different LLM applications
Prompt Engineering
Retrieval Augmented Generation
Finetuning LLMs
Training LLMs from Scratch
Quiz
2
Getting Started with Prompt Engineering
3
Understanding Different Prompt Engineering Techniques
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
Linkedin
Key Takeaways from the Course
Learn to effectively build and implement different LLM applications
Understand and apply few-shot, one-shot, and zero-shot prompting
Gain expertise in building chatbots using enterprise data through advanced prompt engineering methods
Course Name : Building Your First Computer Vision Model - Free Course || Course Description : Building Your First Computer Vision Model - Free Course
Embarking on a career in Computer Vision can be straightforward with the right guidance. This course provides an ideal pathway to master the complexities of image data analysis.
Enroll for free
34 Mins
4.6/5
Beginner
Introduction to Computer Vision using PyTorch
This course will help you gain a deep understanding of Computer Vision and build advanced CV models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming a Computer Vision expert. Master the techniques to build convolutional neural networks, and classify images.
Who Should Enroll:
Professionals: Individuals looking to expand their skill set and leverage CV across different industries.
Aspiring Students: For those setting out on their journey to master image data analysis and leave a mark in the tech world.
Course curriculum
1
Introduction to Computer Vision
Pixel Perfect - Decoding Images
Understanding a CNN - Convolutional Layer
Hands on - Image Processing Techniques
Understanding a CNN - Striding and Pooling
Understanding a CNN - Pooling Layer
Understanding AlexNet and Building a CNN Model
Quiz
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Instructor
Apoorv Vishnoi, Head - Training Vertical, Analytics Vidhya
Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.
Key Takeaways from the course
Learn Computer Vision techniques and build real-world CV Models.
Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
Course Name : Bagging and Boosting ML Algorithms - Free Course || Course Description : Bagging and Boosting ML Algorithms - Free Course
This free course on Advanced ML Algorithms - Bagging and Boosting is perfect place to get a taste of how advanced ML algorithms look like and function on a real-world business problem.
Enroll for free
Course curriculum
1
Bagging
Resources to be used in this course
Problem Statement
Understanding Ensemble Learning
Introducing Bagging Algorithms
Hands-on to Bagging Meta Estimator
Introduction to Random Forest
Understanding Out-Of-Bag Score
Random Forest VS Classical Bagging VS Decision Tree
Project
2
Boosting
Bagging and Boosting ML Algorithms
This course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. You'll learn to apply algorithms like Random Forest, AdaBoost, and Gradient Boosting to a real-world dataset, equipping you with the skills to improve predictive accuracy and robustness in your projects.

Who Should Enroll:
Professionals: Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domains
Aspiring Students: Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses
Key Takeaways from the Course
Lear to effectively use Bagging and Boosting Algorithms
Hands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize these algorithms
Icons & text
A working laptop/desktop and an internet connection
Knowledge of Basic ML (Regression and Decision Tress)
Understanding of Python Programming Language
Course Name : MidJourney: From Inspiration to Implementation - Free Course || Course Description : MidJourney: From Inspiration to Implementation - Free Course
Understand the fundamentals of the famous image generation tool - Midjourney in this free course. You will learn the various components of Midjourney and how to use it to bring your imaginations to real world.
Enroll for free
33 Mins
4.6/5
Intermediate
MidJourney: From Inspiration to Implementation
This course will provide you with a practical understanding of MidJourney tools. By the end of the course, you will be able to utilize MidJourney effectively and explore alternative tools for your creative projects. You'll learn how to draw inspiration, use MidJourney's features, and understand its applications through engaging lessons.

Who Should Enroll:
Creative Professionals: Individuals looking to enhance their creativity and apply MidJourney tools to various artistic and visual projects.
Aspiring Creatives: Those beginning their journey into visual storytelling and digital art, seeking to learn the fundamentals of MidJourney and its alternatives.
Course curriculum
1
MidJourney
MidJourney - Storm _ Story
MidJourney - Inspiration
MidJourney - How to use
MidJourney Alternatives
Quiz
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
What do I need to start the course
A working laptop/desktop and an internet connection
Some basic Knowledge of Stable Diffusion
Basic understanding of Prompt Engineering
Instructor
Sandeep Singh, Expert Senior Director, Bain & Company
Sandeep Singh, expert senior director at Bain & Company is a leader in AI and Computer Vision. He has pioneered advanced geospatial solutions in Silicon Valley, enhancing mapping, navigation, and sector-wide applications.
Key Takeaways from the course
Understand MidJourney Tools: Gain a practical understanding of how to use MidJourney for creative projects.
Find Inspiration: Learn how to draw inspiration from various sources to fuel your creativity.
Explore Alternatives: Discover alternative tools to broaden your creative toolkit.
Course Name : Creating Problem-Solving Agents using GenAI for Action Composition || Course Description : Creating Problem-Solving Agents using GenAI for Action Composition
Discover how Generative AI is revolutionizing Agentic AI systems to solve complex real-world problems.
Enroll for free
Course Description
This introductory course provides a concise overview of Agentic AI systems, covering their evolution, current state, and practical applications. You will explore key topics including the history of Agentic AI systems, the role of agents today, multi-agent systems, and practical solutions for implementing them. Perfect for those seeking a foundational understanding of intelligent Agentic AI systems in action.
Course curriculum
1
Creating Problem-Solving Agents using GenAI for Action Composition
Introduction
Overview- Count the Number of Agents
A brief history of Agentic Systems
Agents Today
Multi-Agent Systems Today
Practical Solutions
Who should Enroll?
Beginners in AI and ML looking to understand agentic systems and their real-world applications.
Tech enthusiasts and developers interested in learning the basics of creating intelligent, problem-solving agents.
Professionals exploring multi-agent systems for automation and dynamic task orchestration in various domains.
Key Takeaways
Understand the evolution and current capabilities of agentic systems.
Learn the basics of agents and multi-agent systems, including their role in solving complex problems.
Gain insights into practical applications of agents and how they function in real-world scenarios.
About the Instructor
Vikas Agrawal - Senior Principal Data Scientist at Oracle Analytics Cloud
Vikas Agrawal is a Senior Principal Data Scientist at Oracle Analytics Cloud, focused on designing and deploying AI solutions across ERP, SCM, HCM, CX, and MFG for Fusion and NetSuite customers. He ensures that AI models are automatically generated, updated, and tailored to each customer's evolving data. His research involves developing intelligent agents that leverage domain knowledge to solve complex problems by combining tools and critiqued LLMs/LMMs for hypothesis generation and task orchestration. An electrical engineer turned computer scientist, Vikas is an IIT Delhi graduate with experience at CalTech, Intel, and Infosys.
Frequently Asked Questions (FAQs)
What is an agentic system?
An agentic system refers to an AI-based structure where agents can autonomously perform tasks, often in collaboration, to solve complex problems.
Do I need prior AI knowledge to take this course?
No, this course is beginner-friendly and provides an introduction to agentic systems and their applications.
Are there any hands-on projects in this course?
Yes, you will see practical demos that illustrate how agents are created and used in real-world applications.
How will this course benefit me professionally?
You'll gain foundational knowledge of AI agents, enabling you to apply these concepts to automation, AI development, and other tech projects involving intelligent systems.
Course Name : Improving Real World RAG Systems: Key Challenges & Practical Solutions || Course Description : Improving Real World RAG Systems: Key Challenges & Practical Solutions
Master key challenges in real-world Retrieval-Augmented Generation (RAG) systems. Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
Enroll for free
Course Description
This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.
Course curriculum
1
Improving Real World RAG System
Introduction to RAG Systems
Resources
RAG System Challenges Practical Solutions
Hands-on: Solution for Missing Content in RAG
Other Key Challenges
Practical Solutions
Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed
Wrong Format Problem Solution
Hands-on: Solution for Wrong Format
Incomplete Problem Solution
HyDE
Other Practical Solutions from recent Research Papers
Who should Enroll?
AI/ML professionals aiming to enhance RAG system performance and solve real-world challenges.
Developers/Engineers building search, conversational, or generative AI systems needing better data retrieval and context handling.
Researchers/Enthusiasts seeking hands-on experience with advanced RAG techniques and agentic systems.
Key Takeaways
Master RAG systems with a solid grasp of architecture and components.
Solve key challenges like missing content and hallucinations.
Optimize performance with advanced chunking and retrieval strategies.
Develop practical decision-making skills for LLM adoption in various industries.
About the Instructor
Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya
Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include "Top 10 Data Scientists in India, 2020," "40 under 40 Data Scientists, 2021," "Google Developer Expert in Machine Learning, 2019," and "Top 50 AI Thought Leaders, Global AI Hub, 2022," alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022.
Frequently Asked Questions (FAQs)
What prior knowledge is required?
A basic understanding of AI/ML principles is needed, along with some experience in machine learning frameworks such as PyTorch or TensorFlow. Familiarity with natural language processing (NLP) concepts will be helpful but not mandatory.
Will there be hands-on exercises?
Yes, the course provides practical, hands-on exercises through demos and notebooks. You’ll have opportunities to implement RAG systems and experiment with real-world use cases, focusing on improving retrieval and generation tasks.
What tools or software will I need?
You’ll need access to Python, Jupyter Notebooks, and relevant libraries such as LangChain, Hugging Face, and vector databases. The course will guide you through setting up the necessary environment for practicing the techniques.
How is this course different from other AI courses?
Unlike general AI/ML courses, this course zeroes in on Retrieval-Augmented Generation (RAG) systems, addressing practical challenges like hallucinations, retrieval errors, and context optimization, with a strong emphasis on real-world applications.
Will I learn advanced techniques?
Yes, the course covers advanced techniques like hyperparameter tuning, chunking strategies, embedding models, context compression, and agentic RAG systems, giving you the tools to build and optimize high-performing RAG systems.
Course Name : Framework to Choose the Right LLM for your Business || Course Description : Framework to Choose the Right LLM for your Business
This course provides a comprehensive framework for selecting the right LLM for your business. Learn to evaluate LLMs based on accuracy, cost, scalability, and more, while exploring real-world applications to make informed, strategic AI decisions.
Enroll for free
Course Description
This course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively.
Course curriculum
1
Introduction
Introduction
2
It's an LLM World!
3
Understand Your Business
4
Framework to Choose the Right LLM
5
Case Studies
6
Conclusion
Who should Enroll?
Business leaders seeking to implement AI-driven solutions efficiently.
Data scientists exploring LLMs for industry-specific applications.
Tech professionals involved in AI integration and decision-making processes.
Key Takeaways
Understand how to evaluate and select the right LLM for business needs.
Learn to assess LLMs based on accuracy, cost, scalability, and integration.
Gain insights into real-world LLM applications through case studies.
Develop practical decision-making skills for LLM adoption in various industries.
About the Instructor
Rohan Rao - Principal Data Scientist, H2O.ai; Quadruple Kaggle Grandmaster
A Principal Data Scientist at H2O.ai and an IIT-Bombay alumnus, is a highly accomplished professional. He is a quadruple Kaggle Grandmaster and was formerly ranked #1 on AnalyticsVidhya. In addition to his expertise in data science, Rohan is a 9-time National Sudoku Champion. A versatile individual, he is also a passionate coder, reader, writer, and lifelong learner, known in the community as "vopani."
FAQ
What factors should be considered when choosing an LLM for business?
Key factors include the LLM's accuracy, cost, scalability, technical compatibility, support, security, and compliance with privacy laws. The decision-making framework ensures the chosen LLM aligns with specific business requirements.
Can any LLM solve all business problems?
No, different LLMs are suited to different tasks. Selecting the right LLM depends on the specific business problem, required capabilities, and available resources.
How important is the accuracy of an LLM for business use?
Accuracy is crucial, especially in fields like healthcare and education. LLMs must perform reliably across datasets, ensuring consistency and stability in results for critical business applications.
What are the key decisions for using LLMs in healthcare?
Key decisions include choosing an LLM fine-tuned for medical data, ensuring accuracy, maintaining privacy, and complying with healthcare regulations.
Are open-source LLMs viable alternatives to closed-source options?
Yes, open-source LLMs, like Llama3, can be viable alternatives, especially when customized to specific business needs. They are catching up with closed-source options in performance.
Course Name : Building Smarter LLMs with Mamba and State Space Model || Course Description : Building Smarter LLMs with Mamba and State Space Model
Master Mamba's selective state space model for LLMs. Discover key components like the Mamba block, optimizing sequence modeling with efficient, scalable training and inference, surpassing traditional Transformers.
Enroll for free
Course Description
Unlock the Power of State Space Models (SSM) like Mamba with our comprehensive course designed for AI professionals, data scientists, and NLP enthusiasts. Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures.
Course curriculum
1
Course Overview
Course Overview
2
An Alternative to Transformers
3
Understanding State Space Models
4
Mamba - A Selective State Space Model
Who should Enroll?
AI and ML professionals looking to specialize in State Space Models and Mamba architecture.
Data scientists interested in exploring advanced Generative AI models and architectures.
NLP practitioners who want to integrate SSMs like Mamba in their workflows and use cases.
Key Takeaways
A comprehensive understanding of State Space Models (SSM)
In-depth exploration of The Mamba Architecture
Visual guides and workflows on SSM and Mamba
Advanced applications, comparisons and practical use cases.
About the Instructor
Maarten Grootendorst - Senior Clinical Data Scientist, IKNL; Creator of KeyBERT and BERTopic
Marteen holds three master’s degrees in Organizational, Clinical Psychology, and Data Science, using them to simplify machine learning for a broad audience. As co-author of Hands-On Large Language Models and through popular blogs, he’s reached millions by explaining AI, often from a psychological lens. He’s also the creator of widely-used open-source packages like BERTopic, PolyFuzz, and KeyBERT, which have millions of downloads and are utilized by data professionals globally.
FAQ
What are State Space Models (SSM) in machine learning?
State Space Models (SSM) are used in machine learning to model and predict systems that evolve over time. They represent the system's state as a dynamic process, helping to capture temporal patterns in data, making them useful for tasks like time series forecasting, control systems, and natural language processing.
How do State Space Models differ from traditional RNNs?
State Space Models (SSM) and traditional Recurrent Neural Networks (RNNs) both handle sequential data, but they differ in approach. SSMs use a mathematical framework to model the system's state and evolution over time explicitly. In contrast, RNNs use neural networks to implicitly learn patterns in sequences without explicitly modeling the system's state.
What is the Mamba architecture in AI?
Mamba is an alternative AI architecture designed to address the limitations of traditional transformers. It enhances efficiency with optimizations like RMSnorm and offers significant improvements in inference speed—up to 5× higher throughput. Mamba also scales linearly with sequence length, making it highly effective for handling real-world data, even with sequences up to a million tokens. As a versatile backbone, Mamba achieves state-of-the-art performance across various domains, including language, audio, and genomics. Notably, the Mamba-3B model outperforms transformers of the same size and rivals those twice its size in both pretraining and downstream evaluation.
How does Mamba compare to transformer models?
Mamba architecture differs from traditional transformer models by leveraging state-space models (SSMs) instead of the self-attention mechanism. This key difference allows Mamba to achieve linear complexity scaling with sequence length, a significant improvement over the quadratic scaling seen in transformers. While transformers excel in parallel processing with self-attention, Mamba's use of SSMs enables it to handle sequences more efficiently, especially in tasks involving long sequences, while still supporting parallel processing during training.
What are the applications of State Space Models in NLP?
State Space Models (SSM) are used in NLP for similar applications as other Language Models (LLMs), such as predicting and modeling sequential language patterns. However, SSMs stand out due to their ability to handle long text sequences more efficiently, making them particularly advantageous in tasks that involve processing extensive dependencies within the text.
How do transformers work in large language models?
Transformers use self-attention mechanisms to process input data in parallel, allowing large language models to efficiently learn relationships between words in a sequence, improving performance in tasks like translation and text generation.
Why are RNNs considered in AI despite transformer popularity?
RNNs are still used in AI because they excel at handling sequential data with strong temporal dependencies, and their simpler architecture can be advantageous in specific applications where transformers might be overkill.
What are the benefits of using Mamba in deep learning?
Mamba architecture offers improved efficiency in deep learning models, particularly in handling complex tasks and large-scale AI applications, making it a powerful alternative to traditional transformers.
How do State Space Models improve the accuracy of AI models, such as RNNs or Transformers in processing temporal data?
State Space Models improve AI accuracy by explicitly modeling the underlying state of a system over time, leading to better predictions and more interpretable results, especially in time-sensitive applications.
Course Name : Generative AI - A Way of Life - Free Course || Course Description : Generative AI - A Way of Life - Free Course
Embark on a journey into Generative AI for beginners. Learn AI-powered text and image generation, use top AI tools, and explore industry applications. Gain practical skills, understand ethical practices, and master prompting techniques.
Enroll for free
6 Hours
4.7/5
Beginner
Generative AI - A Way of Life
This course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL·E3. Learn practical applications across industries, ethical considerations, and best practices. Whether you're a content creator, business innovator, or AI enthusiast, gain the expertise to harness Generative AI's full potential and drive innovation in your field.
Course curriculum
1
Introduction to Generative AI
Fundamentals of Generative AI
What is Generative AI?
How does Generative AI work?
Exploring the Potential of Generative AI
GenAI Pinnacle Program
Hands On: Let’s get generating!
2
Text Generation Using Generative AI
3
Image Generation Using Generative AI
Enroll for Free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Who Should Enroll:
This course is perfect for beginners with no technical background, professionals looking to enhance their AI skills, students eager to explore AI, and content creators seeking to leverage AI tools. If you're curious about AI's potential and want to stay ahead in your field, this course is for you.
Instructor
Apoorv Vishnoi, Head - Training Vertical, Analytics Vidhya
Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.
Common Questions Beginners Ask About Generative AI
What is Generative AI?
Generative AI refers to algorithms that can create new data, such as images, text, and music, by learning patterns from existing data.
Do I need to know programming to learn Generative AI?
Having a basic understanding of Python programming is not compulsory, but if you are interested in learning Python there's also a free course available on our platform.
What are the real-world applications of Generative AI?
From AI art to content creation tools and personalized marketing campaigns, Generative AI is revolutionizing industries worldwide.
How does Generative AI differ from traditional AI?
Traditional AI focuses on classification and prediction, while Generative AI creates new content or data based on learned patterns.
Is this course suitable for beginners?
Absolutely! This course is designed for all levels, including those new to machine learning and deep learning.

1 Hour
4.6/5
Advanced
Key Takeaways from the “Nano Course: Dreambooth-Stable Diffusion for Custom Images”
Finetune Dreambooth on the custom dataset discussing each step in detail
Understand the training process of dreambooth
Learn Tricks to Name Your Concept Uniquely in dreambooth
Overview of Stable Diffusion, its journey and training process.
Understand the difference between stable diffusion and dreambooth
Course curriculum
1
Dreambooth-Stable Diffusion for Custom Images
The Current Landscape of Generative AI
Why Stable Diffusion
Recap on History of Stable Diffusion
Intuition behind Stable Diffusion
How to train a Stable Diffusion model
Introduction to Dreambooth
Understanding the Dreambooth Process
Tricks to Name Your Concept Uniquely
How to Select Images for Finetuning Dreambooth
Setting up the Training Environment
Code-Finetuning Dreambooth model on Custom Dataset
The Importance of Captioning in Dreambooth
Differences between Stable Diffusion and Dreambooth
Instructor
Sandeep Singh, Head of Applied AI/Computer Vision at Beans.ai
Highly skilled AI leader with expertise in Generative AI, Computer Vision, NLP, and more. Proven record in developing and optimizing AI projects at scale and Exceptional at team building. A visionary leader fostering innovation and agile culture.
Course Name : A Comprehensive Learning Path for Deep Learning in 2023 || Course Description : A Comprehensive Learning Path for Deep Learning in 2023
Here is a free learning path for people who want to become a Deep Learning expert in 2023.
Enroll for free
About the course
The most common question we get from beginners in the field of Deep Learning is - Where to begin? The journey to becoming a Deep Learning expert can be difficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.
We are here to help you take your first steps into the world of Deep Learning. Here is a free learning path for people who want to become a Deep Learning expert in 2023. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist.
Why take this course?
The course is ideal for beginners in the field of Deep Learning. Several features which make it exciting are:
Beginner friendly course
The course assumes no prerequisites and is meant for beginners
Curated list of resources to follow
All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons.
Pre-requisites
This is a beginner friendly course and has no prerequisites.
Course curriculum
1
January 2023
Getting Started
Overview of the Learning Path
Month-on-Month Plan
Introduction to Deep Learning
Applications of Deep Learning
Setting up your System
Descriptive Statistics and Probability
Python
Exercise : Python
AI&ML Blackbelt Plus Program (Sponsored)
2
February 2023
3
March 2023
4
April 2023
5
May 2023
6
June 2023
7
July 2023
8
August 2023
9
September 2023
10
October 2023
11
November 2023
12
December 2023
Instructor
FAQ
What web browser should I use?
Our training platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above. See our list of supported browsers for the most up-to-date information.
How much do I need to pay for this course?
Nothing! Yes - you read it right. This course is free for our community members as a way to get them started in Data Science.
Do I get certificate upon completion of the course?
No, we do not provide certificate with this course.
Where do I ask my queries?
You can post your queries on the discussion for the course or share them on the discuss portal at discuss.analyticsvidhya.com
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Support for A Comprehensive Learning Path for Deep Learning in 2023
Support for A Comprehensive Learning Path for Deep Learning in 2023 course can be availed through any of the following channels:
Phone - 10 AM - 6 PM (IST) on Weekdays Monday - Friday on +91-8368253068
Email training_queries@analyticsvidhya.com (revert in 1 working day)
Course Name : A Comprehensive Learning Path to Become a Data Scientist in 2024 || Course Description : A Comprehensive Learning Path to Become a Data Scientist in 2024
Want to become a data scientist this year, but confused about where to start and what to follow? This comprehensive learning path from Analytics Vidhya should provide you with all the answers you need!
Enroll for free
2 Hours
4.8/5
Beginner
About the course
Where do I begin? Data science is such a huge field - where do you even start learning about Data Science?
These are career-defining questions often asked by data science aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.
Don’t worry, we are here to help you take your first steps into the world of data science! Here’s the learning path for people who want to become a data scientist in 2023. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data scientist.
Moreover, we have added the most in-demand skills for the year 2023 for data scientists including storytelling, model deployment, and much more along with exercises and assignments.
Course curriculum
1
Overview of the Learning Path 2024
Overview of Learning Path
Month-on-Month Plan
Your Personalized Learning Path for Data Science
AI&ML Blackbelt Plus Program (Sponsored)
2
January 2024: Data Science Toolkit
3
February 2024: Data Visualization
4
March 2024: Data Exploration
5
April 2024: Basics of Machine Learning and art of storytelling
6
May 2024: Advanced Machine Learning
7
June 2024: Other Machine Learning Concepts
8
July 2024: Introduction to Deep Learning and Computer Vision
9
August 2024: Basics of Natural Language Processing
10
September 2024: Model Deployment
11
October 2024: Practice and Projects
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
Key takeaways of this course
The course is ideal for beginners in the field of Data Science. Several features which make it exciting are:
Beginner friendly course: This is a beginner-friendly course and has no prerequisites.
Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons.
Updated skillset for 2023: The knowledge of Machine Learning models is important but that won’t set you apart. We have included some of the top unique skills you’ll require to become a data scientist in 2023.
Assignments to test yourself: What’s the best way to test your knowledge? Each module comes with assignments and MCQs to give your memory a boost.
Course Name : Nano Course: Building Large Language Models for Code || Course Description : Nano Course: Building Large Language Models for Code
Learn how to train Large Language Models for Code from Scratch covering each step involved in detail from training data curation to model evaluation. Deep dive into the journey of creating Starcoder, a 15B parameter code generation model.
Enroll for free
Nano Course: Building Large Language Models for Code
In this Free Nano GenAI Course on Building Large Language Models for Code, you will-
Learn how to train LLMs for Code from Scratch covering Training Data Curation, Data Preparation, Model Architecture, Training, and Evaluation Frameworks.
Explore each step in-depth, delving into the algorithms and techniques used to create StarCoder, a 15B code generation model trained on 80+ programming languages.
Understand and learn the best practices to train your own StarCoder on the data

38 Mins
4.7
Intermediate
Key Takeaways from the “Nano Course: Building Large Language Models for Code”
Learn how to train LLMs for code fom scratch
Deep dive into StarCoder journey
Understand algorithms and techniques used at each step involved in development of StarCoder
Learn best practices to train your own StarCoder model on data
Explore the model architecture, training and evaluation frameworks for Code LLMs
Course curriculum
1
Building Large Language Models for Code
Introduction
Agenda
BigCode Community
Training LLMs for Code from Scratch: Training Data Curation
Training Data Formatting and Preprocessing
Model Architecture
BigCode Ecosystem
Training Frameworks
Model Evaluation
Tools and Descendants of StarCoder
Instructor
Loubna Ben Allal, ML Engineer at Hugging Face
Loubna Ben Allal is a Machine Learning Engineer at Hugging Face. She has been working on LLMs for code. She is part of the core team of BigCode that released The Stack dataset, SantaCoder, and StarCoder models.
Course Name : Machine Learning Summer Training || Course Description : Machine Learning Summer Training
In this free Machine Learning Summer Training, you will learn Python, the basics of machine learning, how to build machine learning models, and feature engineering techniques to improve the performance of your machine learning models.
Enroll for free
This is the second step of the Machine Learning Summer Training, want to know more click here.
What is Machine Learning Summer Training?
If you are a college student and looking for summer training, then you are at the right place where Analytics Vidhya is providing its virtual training along with the mega hackathon for students all over the world to compete, win grand rewards and internship opportunities.
Machine Learning Summer Training is an online program to build and enhance your programming and machine learning skills, led by the best industry experts and data science professionals. After completing this training you will be provided with a blockchain enabled certificate by Analytics Vidhya with lifetime validity.
This is the perfect starting point to ignite your fledging machine learning career and take a HUGE step towards your dream data scientist role.
If you haven't enrolled in the program already, Don't wait!
What is Machine Learning?
Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is re-shaping and revolutionizing the world and disrupting industries and job functions globally.
Machine learning is so extensive that you probably use it numerous times a day without even knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.
In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems.
What are the applications of Machine Learning?
Now that you get the hang of it, you might be asking what are some of the examples of machine learning and how does it affect our life? Here are a few examples where we use the outcome of machine learning already:
Smartphones detecting faces while taking photos or unlocking themselves
Facebook, LinkedIn or any other social media site recommending your friends and ads you might be interested in
Amazon recommending you the products based on your browsing history
Banks using Machine Learning to detect Fraud transactions in real-time
What kind of problems can be solved using Machine Learning?
Machine Learning problems can be divided into 3 broad classes:
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
Here is an illustration of these different machine learning problems:

Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future – you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:
Classification Problems: When you want to classify outcomes into different classes. For example – whether a customer would default on their loan or not is a classification problem which is of high interest to any Bank
Regression Problem: When you are interested in answering how much – these problems would fall under the Regression umbrella. For example – what is the expected amount of default from a customer is a Regression problem

Unsupervised Machine Learning: There are times when you don’t want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example – a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here.

Reinforcement Learning: It is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.
What will I learn from this course?
Python libraries like Numpy, Pandas, etc. to analyze your data efficiently.
Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.
Linear Regression, Logistic Regression, and Decision Trees for building machine learning models.
Understand how to solve Classification and Regression problems using machine learning
How to evaluate your machine learning models using the right evaluation metrics?
Improve and enhance your machine learning model’s accuracy through feature engineering
Prerequisites for the Machine Learning Summer Training
This course requires no prior knowledge about Data Science or any tool.
Machine Learning Summer Training Syllabus
In this machine learning summer training, we will be covering the following topics:
Python for Data Science
Importance of Statistics and EDA
Basics of Machine Learning
Evaluation Metrics for Machine Learning models
Feature Engineering techniques

Tools Covered in this Course
Projects covered in this course
1. Customer Churn Prediction
A Bank wants to take care of customer retention for their product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in the next quarter. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.
2. NYC Taxi Trip Duration Prediction
Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.
Course curriculum
1
Overview of the Course
Overview of the Course
FREE PREVIEW
Knowing each other
FREE PREVIEW
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction to Data Science and Machine Learning
3
Setting up your system
4
Introduction to Python
5
Variables and Data Types
6
Operators
7
Conditional Statements
8
Looping Constructs
9
Data Structures
10
String Manipulation
11
Functions
12
Modules, Packages and Standard Libraries
13
Handling Text Files in Python
14
Introduction to Python Libraries for Data Science
15
Python Libraries for Data Science
16
Reading Data Files in Python
17
Preprocessing, Subsetting and Modifying Pandas Dataframes
18
Sorting and Aggregating Data in Pandas
19
Visualizing Patterns and Trends in Data
20
Machine Learning Lifecycle
21
Problem statement and Hypothesis Generation
22
Importance of Stats and EDA
23
Build Your First Predictive Model
24
Evaluation Metrics
25
Preprocessing Data
26
Build Your First ML Model: k-NN
27
Selecting the Right Model
28
Linear Models
29
Project: Customer Churn Prediction
30
Decision Tree
31
Feature Engineering
32
Project: NYC Taxi Trip Duration prediction
33
Feedback
Instructor
FAQ
Who should take the Free Machine Learning Certification Course for Beginners?
This course is meant for people looking to learn Machine Learning. We will start with understanding Python for Data Science, the importance of statistics and EDA, the underlying intuition behind several machine learning algorithms and then go on to solve case studies using Machine Learning concepts.
Do I need to install any software before starting the course?
You will get information about all installations as part of the course.
Do I need to take the modules in a specific order?
It is highly recommended to take the course in the order in which it has been designed to gain the maximum knowledge from it.
Do I get a machine learning certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the Free Machine Learning Certification Course for Beginners.
Course Name : A Comprehensive Learning Path to Become a Data Engineer in 2022 || Course Description : A Comprehensive Learning Path to Become a Data Engineer in 2022
Want to become a data engineer this year, but confused about where to start and what to follow? This comprehensive learning path from Analytics Vidhya should provide you with all the answers you need!
Enroll for free
About the course
Where do I begin? Data Engineering is such a huge field - where do you even start learning about Data Engineering?
These are career-defining questions often asked by data engineering aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.
Don’t worry, we are here to help you take your first steps into the world of data engineering! Here’s the learning path for people who want to become a data engineer in 2022. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data engineer.
Moreover, we have added the most in-demand skills for the year 2022 for data engineers including storytelling, model deployment, and much more along with exercises and assignments.
Key takeaways of this course
The course is ideal for beginners in the field of Data Engineering. Several features which make it exciting are:
Beginner friendly course: The course assumes no prerequisites and is meant for beginners
Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources.
Updated skillset for 2022: The knowledge of Data Engineering is important but that won’t set you apart. We have included some of the top unique skills you’ll require to become a data engineer in 2022.
Pre-requisites
This is a beginner-friendly course and has no prerequisites.
Course curriculum
1
Overview of Learning Path 2022
Overview of Learning Path
Month-on-Month Plan
AI&ML Blackbelt Plus Program (Sponsored)
2
January 2022: Learn Programming
3
February 2022: Learn Relational Databases
4
March 2022: Fundamentals of Linux and Cloud Computing
5
April 2022 : NoSQL Databases
6
May 2022: Hadoop Ecosystem
7
June 2022: Data Warehousing
Instructor
Course Name : Writing Powerful Data Science Articles || Course Description : Writing Powerful Data Science Articles
Are you looking to write and publish your data science article? Or perhaps you are looking for tips and tricks to improve your article’s readability and viewership. This course on writing powerful data science articles is for you!
Enroll for free
Looking to Publish your Data Science Article? Here’s the Perfect Course for you
"Either write something worth reading or do something worth writing." - Benjamin Franklin
The best way to learn any concept, especially in data science, is by writing about it. That not only helps you understand what you learned in more detail, but sharing it with the community helps others understand how a particular data science idea works.
But here’s the thing - most people want to write, but just can’t get past the initial challenges. This might sound familiar to a lot of people:
What should I write about?
Will anyone read my article?
How do I make my article stand out?
Should I even write?

If you’ve ever asked yourself these questions, you’ll find the answers in this free crash course on how to write impactful and awesome data science articles!
What is being covered in this Data Science Writing Crash Course?
This free crash course on writing powerful data science articles is conducted over two sessions of 1-hour each. Here’s the agenda for both sessions:
Session 1: How to Write Powerful Data Science Articles
This is where it all begins!
The power of writing
What should you write about? How to pick a topic your audience will like?
The big question - HOW should you write an article that resonates with your audience?
A quick demo of our writing platform (editor.analyticsvidhya.com)

Session 2: Best Practices - Writing Articles that Grab your Reader’s Attention
This is where we bring it all together.
Tips on how to improve your article’s engagement
Best practices for enhancing your article’s readability
How to increase your article’s reach
Examples, SEO best practices, tips and tricks to increase your article viewership
Course curriculum
1
Welcome to the Data Science Writing Crash Course!
Why did we create this course?
AI&ML Blackbelt Plus Program (Sponsored)
2
Crash Course - How to Write Powerful Data Science Articles
3
Expert Talk: Writing Powerful Data Science Articles with Parul Pandey
4
Next Steps...
Instructor
Course Name : Machine Learning Certification Course for Beginners || Course Description : Machine Learning Certification Course for Beginners
In this free machine learning certification course, you will learn Python, the basics of machine learning, how to build machine learning models, and feature engineering techniques to improve the performance of your machine learning models.
Enroll for free
6 Hours
4.8/5
Beginner
What is Machine Learning?
Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally.
Machine learning is so extensive that you probably use it numerous times a day without knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.
In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them to real-world business problems.
What will I learn from this course?
Python libraries like Numpy, Pandas, etc. to analyze your data efficiently.
Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.
Linear Regression, Logistic Regression, and Decision Trees for building machine learning models.
Understand how to solve Classification and Regression problems using machine learning
How to evaluate your machine learning models using the right evaluation metrics?
Improve and enhance your machine learning model’s accuracy through feature engineering
Prerequisites:
This course requires no prior knowledge of Data Science or any tool.
Projects covered in this course
1. Customer Churn Prediction
A Bank wants to take care of customer retention for their product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in the next quarter. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.
2. NYC Taxi Trip Duration Prediction
Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.
Tools Covered in this Course
Course curriculum
1
Overview of the Course
Overview of the Course
FREE PREVIEW
Knowing each other
FREE PREVIEW
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction to Data Science and Machine Learning
3
Setting up your system
4
Introduction to Python
5
Variables and Data Types
6
Operators
7
Conditional Statements
8
Looping Constructs
9
Data Structures
10
String Manipulation
11
Functions
12
Modules, Packages and Standard Libraries
13
Handling Text Files in Python
14
Introduction to Python Libraries for Data Science
15
Python Libraries for Data Science
16
Reading Data Files in Python
17
Preprocessing, Subsetting and Modifying Pandas Dataframes
18
Sorting and Aggregating Data in Pandas
19
Visualizing Patterns and Trends in Data
20
Machine Learning Lifecycle
21
Problem statement and Hypothesis Generation
22
Importance of Stats and EDA
23
Build Your First Predictive Model
24
Evaluation Metrics
25
Preprocessing Data
26
Build Your First ML Model: k-NN
27
Selecting the Right Model
28
Linear Models
29
Project: Customer Churn Prediction
30
Decision Tree
31
Feature Engineering
32
Project: NYC Taxi Trip Duration prediction
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
FAQs
Who should take the Free Machine Learning Certification Course for Beginners?
Do I need to install any software before starting the course?
Do I need to take the modules in a specific order?
Do I get a machine learning certificate upon completion of the course?
Course Name : Data Science Career Conclave || Course Description : Data Science Career Conclave - Transition to Data Science!
Here’s the perfect course to help you get all your data science career queries answered. Top data science experts expound on what they look for in a data science professional and how you can build those skills.
Enroll Now
Are you looking for a role in the data science space?
You’ve come to the right place!
It feels like half the world wants to move into data science these days, with spectacular perks and a plethora of openings on offer in the industry. Organizations are investing heavily in data science talent to stay or move ahead of their competitors. As a data science aspirant, you couldn’t have picked a better time to change your career!

But this comes with its own set of challenges. We are often asked by folks about how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – they all want a piece of the data science pie.
In this exclusive course called the “Data Science Career Conclave”, Analytics Vidhya has brought together leading data science experts to share their view on a broad range of data science career topics.
What is being covered in this Data Science Career Conclave?
As we said, a broad range of topics related to transitioning into a data science career. Here’s a brief list of topics you can look forward to:
Different Roles in Data Science - Which Role is Right for You? - by Mathangi Sri
What are Hiring Managers Really Looking For? - by Kiran R
How to Build your Digital Profile for Data Science - by Dipanjan Sarkar
Panel Discussion: How can you Transition into Data Science in 12 Months?
Data Science Career Conclave Curriculum
1
Career Conclave
Introduction to the Career Conclave
Different Roles in Data Science - Which Role is Right For You?
What are Hiring Managers Really Looking For?
How to Build your Digital Profile for Data Science
Panel Discussion: How can you Transition into Data Science in 12 Months?
AI&ML Blackbelt Plus Program (Sponsored)
Customer Support for our Courses & Programs
We are there for your support when you need!
Phone - 10 AM - 6 PM (IST) on Weekdays (Mon - Fri) on +91-8368808185
Email training_queries@analyticsvidhya.com (revert in 1 working day)
Discussion Forum - answer in 1 working day
Course Name : Top Data Science Projects for Analysts and Data Scientists || Course Description : Top Data Science Projects for Analysts and Data Scientists
Kick-start your career in data science with our data science courses and projects including learning on projects covering machine learning, computer vision, deep learning, natural language processing, reinforcement learning & data engineering.
Enroll for free
4.6/5
Advanced
A Comprehensive Collection of Open Source Data Science Projects!
“How many data science projects have you completed so far?”
This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.
Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.
And that’s where open-source data science projects play such a key role!
Course curriculum
1
Welcome to the course!
About the Data Science Projects Course
AI&ML Blackbelt Plus Program (Sponsored)
2
Machine Learning Projects
3
Deep Learning Projects
4
Computer Vision Projects
5
Natural Language Processing (NLP) Projects
6
Reinforcement Learning Projects
7
Data Engineering Projects
8
Other Data Science Projects
Enroll for free
Who is the Top Data Science Projects Course for?
This course is for anyone who:
Wants to become a data scientist, data analyst, business analyst, or any other role in the data science space
Wants to practice and work on their existing data science skills
Is curious about the latest state-of-the-art projects in data science
Wants to enhance and improve their data science resume
FAQ
Common questions related to the Top Data Science Projects for Analytics and Data Scientists Course
I have decent programming experience but no background in data science or machine learning. Is this course right for me?

What is the fee for the course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on Top Data Science Projects for Analytics and Data Scientists. What should I learn next?

Can I download the projects in this course?
Course Name : Getting Started with Git and GitHub for Data Science Professionals || Course Description : Getting Started with Git and GitHub for Data Science Professionals
Upskill your data science acumen with Analytics Vidhya's Github course for data scientists that empowers you with learning the value and the ins and out’s of Git and GitHub and using Git and GitHub to make your data science projects easier to track.
Enroll for free
Learn All About Git and GitHub in this Essential Course for Data Scientists
Ever heard of version control? It is one of the most important concepts in a data scientist’s daily role - and yet most newcomers and beginners haven’t even come across it! This is a fallacy you must overcome as soon as possible.
You need to understand how to navigate through Git and GitHub if you want to make it as a data science professional. While a lot of folks know about these tools (having used them for cloning open source code from Google Research and other top data science organizations), they never really understand their real purpose.
The beauty of version control will be akin to a revelation. The way you can create a remote project and have all your team members work on different features parallelly yet independently but still have a stable running code at the end of the day - priceless! A lot of the problem we face in data science while working remotely and independently will be erased with a quick understanding of Git and GitHub.
Yes, this course really is that important!
Highlights of the Git and GitHub Course
Learn the value and the in’s and out’s of Git and GitHub in this comprehensive guide for beginners
No prerequisites required - start from scratch and understand how Git and GitHub work
How you can use Git and GitHub to make your data science projects easier to track
You will add an essential data scientist skill to your portfolio after the course - version control!
Who is the Getting Started with Git and GitHub Course for?
This course is for anyone who:
Wants to become a data scientist or wants a role in data science
Wants to learn all about programming
Is curious about the role Git and GitHub play in data science programming
Wants to add a key data science skill to their resume called version control
What do you need to get started with the Git and GitHub course?
Here’s what you’ll need:
A working laptop/desktop with 4 GB RAM
A working Internet connection
A GitHub account
Course curriculum
1
Getting Familiar with Git and Github
What is Git?
What is Github?
AI&ML Blackbelt Plus Program (Sponsored)
2
Understand Git Terminology
3
Get Started with Git
4
Going Remote - Get started with Github
5
What's next?
FAQ
Common questions related to the Getting Started with Git and GitHub for Data Science Professionals
I have decent programming experience but no background in data science or machine learning. Is this course right for me?
What is the fee for the course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on how to navigate Git and GitHub. What should I learn next?

Can I download the videos in this course?
Enroll in Getting Started with Git and GitHub for Data Science Professionals
More than 1 Million users use Analytics Vidhya every month to learn Data Science. Start your journey now!
Get started now
Course Name : Machine Learning Starter Program || Course Description : Machine Learning Starter Program
Course Name : Data Science Hacks, Tips and Tricks || Course Description : Data Science Hacks, Tips and Tricks
Learn key data science hacks, tips and tricks to become a better and more efficient data scientist. These data science hacks cover a wide range of data science topics like speeding up Python code, optimizing your data science algorithm and much more!
Enroll for free
Do you want to write more efficient Python code? Want to become a better programmer? How about speeding up your data science tasks? This Data Science Hacks, Tips and Tricks course is for you!
The Data Science Hacks, Tips and Tricks course is your one stop destination to become a better and more efficient data scientist!
We have poured in our decades of experience with data science and programming (especially Python programming!), to provide you with time-saving hacks related to:
Python tips and tricks
Data exploration tips and tricks
Data preprocessing hacks
Efficient use of Jupyter notebooks
Python functions
Building predictive models (hacks to build machine learning models in no time!),

And much more!
We have created the Data Science hacks, tips and tricks course in a way that you can go through each hack as a separate module. Since the goal of the hacks, tips and tricks is to provide you with efficient code to solve problems, the videos are a demo of these hacks, tips and tricks. The videos are self-explanatory.
This free course by Analytics Vidhya covers a broad range of data science hacks, tips and tricks, including Python programming hacks, tips and tricks to ace data science tasks like data preprocessing and data exploration, and much more. Get started today!
What is covered in the data science hacks, tips and tricks course?
Data Exploration Hacks - Do you know how to get a full report of your dataset in just 1 line of code? Explore the data like a pro. Understand and practically work on data with shorter lines of code.
Python hacks, tips and tricks - Python is simple to understand language and is the go-to language to implement machine learning. Learn the ‘pythonic’ way to code in this course
Data Visualization Tips and Tricks - Visualizing data in the right way can be the make-or-brake situation in your meeting with your seniors. To be able to tell a story through the use of different visualizations. In these visualization hacks, tips and tricks we explore different libraries and their different modules and how to implement them. Did you know you can make interactive plots using pandas dataframe in just one line of code? We’ll see this data science hack in this course
Data Preprocessing Hacks - Data preprocessing is a really important step before model building. Standardization, normalization, encoding categorical variables are just a few of these. You’ll learn which functions to use and tuning which parameters would be the most suitable for you
Jupyter Notebook hacks, tips and tricks - In jupyter notebook, you can write in multiple lines at the same time using multicursor, change the theme of your notebook, display multiple outputs for the same cell. These tricks and tips help you to focus on what’s really important and that is data analysis and discards unnecessary hassle
Other Data Science and Machine Learning Hacks - Hacks related to machine learning algorithms, hyperparameter tuning, evaluating your machine learning model. Using these hacks you’ll be able to identify new methods to take your data science skills to the next level!
Who should take the Data Science Hacks, Tips and Tricks course?
The beauty of this course is that it’s designed for a broad range of audience. Everyone could do with these data science hacks, tips, and tricks! We’re all involved at some stage of the data science pipeline so this course, and the hacks we showcase, will help you out for sure.
These Data Science hacks, tips and tricks are meant for:
Data scientists (aspiring data scientists, established data scientists - it’s meant for all levels!)
Data analysts
Business analysts
Data science team leads
Machine learning enthusiasts
Data engineers
And anyone who is curious about writing efficient code and building quicker machine learning models!
Enroll for free
Course curriculum
1
Introduction to Data Science Hacks, Tips and Tricks Course
About the Data Science Hacks, Tips and Tricks Course
AI&ML Blackbelt Plus Program (Sponsored)
2
Data Science Hack #1 - Resource Downloader
3
Data Science Hack #2 - Pandas Apply
4
Data Science Hack #3 - how to extract email addresses from text?
5
Data Science Hack #4 - Pandas Boolean Indexing
6
Data Science Hack #5 - Pandas Pivot Table
7
Data Science Hack #6 - Splitting a String in Python
8
Data Science Hack #7 - Transforming distributions to Normal Distributions
9
Data Science Hack #8 - Remove Emojis from text
10
Data Science Hack #9 - Elbow method for kNN classifier
11
Data Science Hack #10 - Pandas crosstab for quick exploratory analysis
12
Data Science Hack #11 - Scaling features using MinMax Scaler
13
Data Science Hack #12 - Feature Engineering for Date Time Features
14
Data Science Hack #13 - Creating dummy test data using sklearn
15
Data Science Hack #14 - Image Augmentation to increase size of Training data
16
Data Science Hack #15 - Fast Tokenization using Hugging Face
17
Data Science Hack #16 - Stratified sampling using sklearn
18
Data Science Hack #17 - Reading html files using Pandas read_html
19
Data Science Hack #18 - Extract different data types into different dataframes
20
Data Science Hack #19 - Pandas profiling for quick exploratory analysis
21
Data Science Hack #20 - Change wide form dataframe to Long form dataframe
22
Data Science Hack #21 - Magic functions in Jupyter notebooks
23
Data Science Hack #22 - Set Jupyter theme
24
Data Science Hack #23 Change Cell width in Jupyter notebook
25
Data Science Hack #24 - Change Datatype to datetime
26
Data Science Hack #25 - Sharing jupyter notebook
27
Data Science Hack #26 - Visualize Decision Tree
28
Data Science Hack #27 - Invert Dictionary in Python
29
Data Science Hack #28 Visualize Interactive plot
30
Data Science Hack #29 - Write python file directly from jupyter notebook cell
31
Data Science Hack #31 Feature Selection
32
Data Science Hack #32 Convert string into characters
33
Data Science Hack #33 Apply pandas in parallel
34
Data Science Hack #34 Convert Date format
35
Data Science Hack #35 Make images of same size
36
Data Science Hack #36 Regex testing and debugging
FAQ
Common questions related to the Data Science Hacks, Tips and Tricks course
Who should take the Data Science Hacks, Tips and Tricks course?
This course is for people who want to write more efficient data science code, learn programming hacks, or anyone who is curious about the different tips and tricks you can employ in a data science project!
What is the fee for this course?
This course is free of cost!
How long would I have access to the “Data Science Hacks, Tips and Tricks” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
How much effort will this course take?
You can complete the "Data Science Hacks, Tips and Tricks" course in a few hours. As we mentioned above, each module covers a different hack, tip or trick so you can choose as per your convenience.
Where can I apply these data science hacks?
Anywhere in your data science journey! These hacks, tips and tricks cover a broad range of topics so you can apply them wherever it fits. We suggest getting started with the practice problems on Analytics Vidhya’s DataHack Platform.
I’ve completed this course already. What should I learn next?
That’s great! Please note that we are regularly updating the course with new hacks, tips and tricks so keep checking back to get your daily supply!
Also, we recommend the “Applied Machine Learning” course as the next step in your journey. You will work on real world hands-on data science case studies, learn the fundamentals of machine learning, and a whole host of other things.
Can I download videos from this course?
We regularly update the "Data Science hacks, tips and tricks" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.
Which programming language is used in this course?
We are primarily using Python to showcase these data science hacks. You might see a bit of R sprinked in from time to time!
Instructor(s)
Course Name : Introduction to Business Analytics || Course Description : Introduction to Business Analytics
Business analytics is thriving – and so is its role in forward-thinking organizations around the world. The demand for business analytics professionals is growing multifold - and now is the time to start working towards your desired career.
Enroll for free
1 Hour
4.6/5
Beginner
About Introduction to Business Analytics
Getting Started with Business Analytics
What is Business Analytics? Why has it become so popular recently? What are some of the popular applications of Business Analytics? And more importantly, how can you get started with learning Business Analytics from scratch?
With growth in digitisation, Business Analytics is ubiquitous right now. Organizations are splurging to integrate data science solutions in their daily processes. This is where they need Business Analysts.
Why pursue Business Analytics:
Data is ubiquitous! Organizations need people who can use Business Analytics tools and techniques to make sense of this data.
It is one of the hottest field in the industry right now
There are so many Business Analytics tools and techniques which can be applied to solve business problems. Keep learning, keep growing!
The potential of Business Analytics is limitless - spanning across industries, roles and functions
Course Curriculum
1
What is Business Analytics?
What is Business Analytics?
Quiz: What is Business Analytics
You just joined an exicting startup!
Quiz - Map the Job families
Data Scientist vs. Data Engineer vs. Business Analyst
Quiz - Map the responsibilities
Sample problems and projects - Business Analytics vs. Data Science
Quiz: Sample problems and Projects - Business Analysts vs. Data Scientits
A few more things - Business Analytics vs. Data Science
Career in Business Analytics
Knowing Each other
AI&ML Blackbelt Plus Program (Sponsored)
2
Spectrum of Business Analytics
3
Skills Required for Business Analytics and Roadmap of Business Analytics Program
4
Case study: Ezine Publishing
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
FAQs
What is the fee for this course?
Can I download the videos from this course?
Do I need to take the modules in a specific order?
Do I get a certificate upon completion of the course?
Course Name : Introduction to PyTorch for Deep Learning || Course Description : Introduction to PyTorch for Deep Learning
PyTorch is a popular and leading deep learning framework. But what exactly is PyTorch? How does PyTorch work? How can you use PyTorch to build deep learning models? This PyTorch tutorial course will help you answer these questions in detail.
Enroll for free
1 Hour
4.6/5
Beginner
PyTorch for Deep Learning - A Game Changing Deep Learning Framework
Welcome to the world of PyTorch - a deep learning framework that has changed and re-imagined the way we build deep learning models.
PyTorch was recently voted as the favorite deep learning framework among researchers. It has left TensorFlow behind and continues to be the deep learning framework of choice for many experts and practitioners.
PyTorch is super flexible and is quite easy to grasp, even for deep learning beginners. If you work on deep learning and computer vision projects, you’ll love working with PyTorch.
As a beginner in deep learning and PyTorch, you’ll inevitably have a lot of questions:
What is PyTorch?
Why should you learn PyTorch?
How does PyTorch work?
How can you install PyTorch?
PyTorch vs. TensorFlow vs. Keras - which deep learning framework is the best?
What are the advantages of using PyTorch?
What are some challenges you might face when using PyTorch?
What kind of deep learning projects can you solve using PyTorch?
Is PyTorch relevant in the industry?
Do you need to know deep learning to learn PyTorch?
What kind of neural networks can you build using PyTorch?
Which programming language works best with PyTorch?

Course Curriculum
1
What is PyTorch?
Getting Started with PyTorch
Why should we use PyTorch?
A word from the creators of PyTorch
Tensors in PyTorch
Mathematical Operations in PyTorch(vs. NumPy)
Matrix Operations in PyTorch(vs. NumPy)
Tensor Operations
AI&ML Blackbelt Plus Program (Sponsored)
2
Neural Networks
3
Implementing a Neural Network in Pytorch
4
Deep Learning on Pytorch
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Common Questions Deep Learning Beginners Ask About PyTorch
What is PyTorch?

PyTorch is a Python-based library that provides maximum flexibility and speed. We’ve found PyTorch to be as simple as working with NumPy! You’ll figure this out inside the course for yourself.

We highly recommend learning PyTorch right now - it is quickly becoming the framework of choice for deep learning practitioners. PyTorch will be adopted by the industry soon as well so get on board today!

Why should you learn PyTorch?

PyTorch is one of the most popular and upcoming deep learning frameworks that allows you to build complex neural networks. It is rapidly growing among the research community and companies like Facebook and Uber are using it as well.
Here is what Andrej Karpathy, head of AI at Tesla, has to say about PyTorch - “I've been using PyTorch a few months now and I've never felt better. I have more energy. My skin is clearer. My eyesight has improved.”

PyTorch vs. TensorFlow vs. Keras - which deep learning framework is the best?

This is a common question and a relevant one. There is no shortage of deep learning frameworks out there so which one should you choose?
There’s no one-size-fits-all approach here. Each deep learning framework has its own unique set of features which is why data scientists go for one over the other.
We recommend checking out this article on Analytics Vidhya to understand how PyTorch compares against the other deep learning frameworks like TensorFlow and Keras. This will help you settle the PyTorch vs. TensorFlow debate for sure!

What are the advantages of using PyTorch?

The main advantage of PyTorch is the imperative programming feature - which performs computations on your code as you are typing it, so debugging it is super easy! It is also much faster than NumPy and easy to learn too.
What are some challenges you might face when using PyTorch?

PyTorch is not meant to be an end-to-end deep learning framework and using it for production in the industry remains a challenge. It is also relatively newer than other deep learning frameworks and has only recently released its stable versions.
What kind of deep learning projects can you solve using PyTorch?

You can work on all sorts of deep learning projects using PyTorch! Here are a few examples:
Handwritten Digit Classification
Object and Image Classification
Sentiment Text Classification
Image Style Transfer, among others

You can check out the different datasets and projects to apply PyTorch on Analytics Vidhya’s DataHack platform.
Is PyTorch relevant in the industry?

Absolutely! While it’s still in its nascent stage, PyTorch is quickly becoming the go-to tool of choice for a lot of leading organizations. It’s flexible approach and easy-to-understand style have won over a lot of newcomers and industry veterans.
Do you need to know deep learning to learn PyTorch?

No! PyTorch is a framework - you do not need to know deep learning to learn how it works. It will of course help you to learn both PyTorch and deep learning - they both go hand-in-hand to be truly effective.

Make sure you check out our popular Computer Vision using Deep Learning course to dive in depth into this subject.
What kind of neural networks can you build using PyTorch?

PyTorch is an excellent framework for getting into actual machine learning and neural network building. In fact, in the course, we will be building a neural network from scratch using PyTorch. It is ideal for more complex neural networks like RNNs, CNNs, LSTMs, etc and neural networks you want to design for a specific purpose.
Which programming language works best with PyTorch?

As the name suggests, PyTorch is built on Python. The flexibility and ease- of understanding comes from Python as you’ll see in this Introduction to PyTorch for Deep learning course.
FAQs
Who should take the Introduction to PyTorch for Deep Learning course?
I have decent programming experience but no background in machine learning or deep learning. I have never designed a neural network. Is this course right for me?
What is the fee for the course?
How long would I have access to the “Introduction to PyTorch for Deep Learning” course?
How much effort do I need to put in for this PyTorch course?
I’ve completed this course and have decent knowledge about PyTorch. What should I learn next?
Can I download the videos in this course?
Which programming language is used to teach the Introduction to PyTorch for Deep Learning course?
Course Name : Introduction to Natural Language Processing || Course Description : Introduction to Natural Language Processing
Natural Language Processing (NLP) is the art of extracting information from unstructured text. This course teaches you basics of NLP, Regular Expressions and Text Preprocessing.
Enroll for free
5 Hours
4.7/5
Beginner
Introduction to Natural Language Processing (NLP)
Natural Language Processing is the art of extracting information from unstructured text. Learn basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course.

What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is basically how you can teach machines to understand human languages and extract meaning from text.
Language as a structured medium of communication is what separates us human beings from animals. We are surrounded by text data all the time sourced from books, emails, blogs, social media posts, news and more.
Natural Language Processing is expected to be worth 30 Billion USD by 2024 with the past few years seeing immense improvements in terms of how well it is solving industry problems at scale.

Some Exciting Applications of Natural Language Processing!
Natural Language Processing (NLP) today powers many key real-life industry applications, such as:
Language Translation
Dialog Systems / Chatbots
Sentiment Analysis
Text Summarizers
Speech Recognition
Autocorrect
Course Curriculum
1
Module 1 : Introduction to Natural Language Processing
Welcome to the Course
About the Course
Introduction to Natural Language Processing
Exercise : Introduction to Natural Language Processing
Python for Data Science (Optional)
AI&ML Blackbelt Plus Program (Sponsored)
2
Module 2: Learn to use Regular Expressions
3
Module 3: First Step of NLP - Text Processing
4
Module 4: NLP Certification Exam
5
Module 5: Where to go from here?
Enroll now
Certificate of Completion
Upon successful completion of the course, you will be provided a certificate by Analytics Vidhya with lifetime validity.
Common Questions beginners ask about Natural Language Processing
Is it a good time to pursue Natural Language Processing?
A good time to start with NLP is now! With plethora of applications in several markets & industries, NLP has become a highly sought after skill all over the world.
What is the best language to learn for solving NLP tasks?
Natural Language Processing with Python is the way to go and it has been the most popular language in both industry and Academia. Python provides excellent ready made libraries such as NLTK, Spacy, CoreNLP, Gensim, Scikit-Learn & TextBlob which have excellent easy to use functions to work with text data. Deep Learning frameworks like PyTorch, Tensorflow and Keras which are all part of Python Ecosystem are the default choices for using Deep Learning in Natural Langauge Processing.
Do I need to have a PhD to build a career in NLP?
Applied NLP is something that can be mastered by someone with good knowledge of Python and a background in Engineering or quantitative field is a good to have but not a necessity. Natural Language Processing with Python is something that can be taught and learnt with dedicated effort and a good learning path.
FAQs
Who should take this course?
I have an experience of 2+ years and have no prior knowledge on NLP. Is the course right for me?
If I do not meet the requirements to enroll, what should I do?
What is the fee for this course? Will I get a certificate?
How long would I have access to “Introduction to Natural Language Processing” course?
How much effort will this course take?
How can I apply and test my learnings about Natural Language Processing?
Identify the sentiment for Big Tech Companies
Twitter Sentiment Analysis
Can I download videos from this course?
Which programming language is used to teach Natural Language Processing in this course?
Course Name : Getting started with Decision Trees || Course Description : Getting started with Decision Trees
Decision Tree algorithm is one of the most powerful algorithm in Machine Learning. Decision trees are used by beginners/ experts to build machine learning models. This course provides you everything about Decision Trees & their Python implementation.
Enroll for free
What is a Decision Tree?
A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction / label.

Why learn about Decision Trees?
Decision Trees are the most widely and commonly used machine learning algorithms.
Decision Trees can be used for solving both classification as well as regression problems.
Decision Trees are robust to Outliers, so if you have Outliers in your data - you can still build Decision Tree models without worrying about impact of Outliers on your model.
Decision Trees are easy to interpret and hence have multiple applications in different industries.
What will you learn in Getting Started with Decision Tree Course?
Introduction to Decision Trees - The course starts with basics of Decision Trees, the philosophy behind decision tree algorithm and why they are so popular among data scientists
Terminologies related to decision trees - Don't worry if you don't know anything about Decision Trees - that is the whole point about this course. What is a Leaf Node? Parent Node? Pruning? We will teach you all of these terminologies related to decision tree in a practical manner.
Different splitting criterion for decision tree like Gini, chi-square The course teaches you different splitting criteria like Gini, Information Gain, chi-square and how do they impact the decision tree model
Implementation of decision tree in Python - The course will tell you several best practices you should keep in mind while implementing Decision Tree algorithm
What do I need to start with Decision Tree course?
A working laptop / desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python - check out this Course
first, if you are new to Python

This is all it takes for you to learn one of the most powerful algorithm in Machine Learning.
What are you waiting for?
Enroll for Free Now
Common challenges faced with Decision Trees - solved!
This course addresses practical challenges faced in building Decision Tree models. You will achieve these outcomes by end of this course:
How and when to build a Decision Tree based model?
How to ensure that your decision tree model is not overfitting the data?
How to determine the right depth for decision tree models?
What are some of the common interview questions related to Decision Trees.
Which evaluation criteria should be used for splitting a decision node?
Course curriculum
Getting Started with Decision Trees
1
Getting Started with Decision Tree
Introduction to Decision Tree
Purity in Decision Trees
Quiz: Purity in Decision Trees
Terminologies Related to Decision Trees
Quiz: Introduction to Decision Trees
Terminologies Related to Decision Trees
How to Select the Best Split Point in Decision Trees
Quiz: How to Select the Best Split Point in Decision Trees
Chi-Square
Quiz: Chi-Square
Information Gain
Quiz: Information Gain
Reduction in Variance
Quiz: Reduction in Variance
Optimizing Performance of Decision Trees
Quiz: Optimizing Performance of Decision Trees
Decision Tree Implementation
Dataset: Decision Tree Implementation
Test your Skills: Decision Tree
Where to go from here?
AI&ML Blackbelt Plus Program (Sponsored)
Project - Predict survivors from Titanic tragedy
Use Decision Tree algorithm to identify survivors from Titanic tragedy
You will analyse what kind of people were likely to survive in Titanic tragedy. You will build a model using Decision Tree to predict which passengers survived the tragedy.
Who is teaching Decision Tree Course?
Certificate of Completion
Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
What are our users saying about Getting Started with Data Science
Great Material with Example Usecase
Raghavendra BG
I happened to cross my path on this course during casual web searching . Started the course with lot of apprehension .Content proved me wrong :-) .It was sim...
Read More
Great presentation of concepts
Pankaj Mathur
Simple and well paced course that provides elegant explanation of underlying concepts!
Getting started with decision trees
Augustine Wandera
I now have a good understanding of the basics of decision trees and their applications
Crystal clear concept on Decision Trees how to split the ...
HIJAM GYANESWAR SINGH
After completion of this course, it has helped me to understand when to implement and which algorithms for Decision Tree Spllit to make a model a fit.
Good course for beginners
bhachi bhachi
Good course for beginners to get an understanding of how a decision tree works
Frequently Asked Questions
Common questions related to Getting Started with Decision Trees course
Who should take Getting Started with Decision Trees course?
I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
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How much effort will this course take?
How can I apply and test my learnings about Decision Trees?
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Which programming language is used to teach Decision Trees in this course?
Do I get a certificate upon completion of the course?
I just completed Decision Tree course, what should I do next?
I don't have Python Installed in my machine, what can I do?
How is a Free Course different from a paid course on Analytics Vidhya?
Can I add this project on my resume and use it in my interview?
Enroll in Getting Started with Decision Trees today
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Course Name : Introduction to Python || Course Description : Introduction to Python
Power up your career with the best and most popular data science language, Python. Leverage your Python skills to start your Data Science journey. This course is intended for beginners with no coding or Data Science background.
Enroll for free
1 Hour
4.8/5
Beginner
Learn Python for Data Science
Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?
You have come to just the right place!
Most industry experts recommend starting your Data Science journey with Python
Across biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
Stackoverflow survey for 2019 had Python outrank Java in the list of most loved languages
Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.
Most data scientists have a few go-to libraries for their daily tasks like:
for performing data cleaning and analysis - pandas
for basic statistical tools – numpy, scipy
for data visualization – matplotlib, seaborn
Why Python and how popular is it for Data Science?
Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist's skill set.
It consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.
Over the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modelling.

This python data science course will help you learn Python libraries like Pandas and use them efficiently for data science and data analysis. Are you ready to power up your career and learn the best data science language?
What does a Data Scientist do?
Data Science is an amalgamation of Statistics, Computer Science and specific domain knowledge.
As more and more data gets generated across the world, we need to leverage it to make decisions and improve them.
A data scientist performs operations on the data provided to analyze and interpret it.
Which companies use Python?
Many of the biggest and most popular companies use Python. Some of them are:
Google, NASA, Amazon
Social networking sites like Instagram, Reddit, Quora, etc
Media streaming companies like Netflix and Spotify
Rideshare companies like Uber and Lyft
“Python has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language.”
- Peter Norvig, Director of Research at Google Inc.
So get onboard the Data Science train by learning Python and upskill yourself with one of the Top Data Science Courses offered by Analytics Vidhya!
Course Curriculum
1
Overview of the Course
Overview of the Course
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction to Python
3
Understanding Operators
4
Variables and Data Types
5
Conditional Statements
6
Looping Constructs
7
Functions
8
Data Structure
9
Lists
10
Dictionaries
11
Understanding Standard Libraries in Python
12
Reading a CSV File in Python
13
Data Frames and basic operations with Data Frames
14
Indexing a Data Frame
15
Data Manipulation and Visualization
16
Regular Expressions
17
Cheatsheet for Python
18
Evaluate
19
Feedback
20
Where to go from here?
Enroll for free
Certificate of Completion
Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Common Questions Beginner ask about Python for Data Science Courses?
Do I need to learn coding to learn Python?
If you are totally new to programming, no need to get intimidated by learning a whole new language.
Python is a very easy language to learn:
It does not have a complicated syntax and understanding Python is very intuitive.
You don’t need to be skilled in coding for getting started in Python.
This course is for beginners we will start right from the foundations to performing data analysis tasks in Python.
I am familiar with other Programming Languages like Java/C++. Will this course help me to migrate to Python?
Do you know that Python is essentially a wrapper on C? That is what makes it fast and easy to understand!
Though Python has recently become popular amongst Data Scientists, it was originally a general-purpose language.
Python is still object-oriented and follows many of the paradigms that Java does.
So if you are familiar with the concepts of programming, you can migrate to Python easily with this course.
How much Python do I need to know to enter Data Science?
Though Python has hundreds of libraries and many more functionalities, you don’t need to know all of them for learning Data Science
Rather than becoming an expert in the entire language, you would need to just be acquainted with the basic syntax of Python.
We will also cover the most popular libraries used by Data Scientists and which you would be using too as a future Data Scientist!
What if I don’t have Python installed on my system?
One of the best things about Python is the wide variety of platform that support writing it.
We will provide easy to follow instructions to work with Python using Anaconda, an extremely popular package manager platform. No matter what Operating System you are using, we have you covered with guides for all of them.
What are the most popular open-source libraries that Python supports?
pandas, numpy, scipy, matlplotlib, seaborn are used for Data Science and Data Analysis
scikit-learn, tensorflow, keras are used for basic and advanced machine learning
libraries for deep learning like OpenCV(Computer Vision), NLTK(Natural Language Processing)
Will I be able to apply what I have learnt here to machine learning and data science projects?
The Python for Data Science course is designed to help you completely understand Python and start using it immediately for Data Science projects.
With regular assignments, quizzes and hands-on projects, you will be full equipped with the essential data science skillsets.
FAQs
Who should take this course?
Do I get a certificate upon completion of the course?
I have an experience of 2+ years, but no background in Data Science nor in Programming. Is the course right for me?
What is the fee for this course?
How long would I have access to the “Python for Data Science” course?
How much effort will this course take?
How can I apply and test my learnings about Python?
Can I download the videos from this course?
Which programming languages is used to teach this course?
Course Name : Loan Prediction Practice Problem (Using Python) || Course Description : Loan Prediction Practice Problem (Using Python)
This course is aimed for people getting started into Data Science and Machine Learning while working on a real life practical problem.
Enroll for free
4.7/5
Intermediate
About the course
This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in.
In this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.
By the end of the course, you will have a solid understanding of Classification problem and Various approaches to solve the probem
Pre-requisites
This course assumes that you have familiarity with Python.
Course curriculum
1
Loan Prediction : Practice Problem
Introduction to the Course
Table of Contents
Problem Statement
Hypothesis Generation
Exercise 2 | Discussion
Getting the system ready and loading the data
Understanding the Data
Univariate Analysis
Bivariate Analysis
Missing Value and Outlier Treatment
Evaluation Metrics for Classification Problems
Model Building : Part I
Logistic Regression using stratified k-folds cross validation
Feature Engineering
Model Building : Part II
AI&ML Blackbelt Plus Program (Sponsored)
Enroll for free
FAQs
Who should take this course?
Do I need to install any software before starting the course?
What is the refund policy?
Do I need to take the modules in a specific order?
Do I get certificate upon completion of the course?
What is the fee for this course?
Is there any placement support?
Course Name : Big Mart Sales Prediction Using R || Course Description : Big Mart Sales Prediction Using R
This course is aimed for people getting started into Data Science and Machine Learning while solving the Big Mart Sales Prediction problem.
Enroll for free
4.6/5
Intermediate
About the course
Sales prediction is a very common real life problem that each company faces at least once in its life time. If done correctly, it can have a significant impact on the success and performance of that company.
In this course you will be working on the Big Mart Sales Prediction Challenge.
The course will equip you with the skills and techniques required to solve regression problems in R. You will be provided with sufficient theory and practice material to hone your predictive modeling skills.
Pre-requisites
This course assumes that you have familiarity with R.
Course curriculum
1
Big Mart Sales
Overview of the Course
Table of contents
Problem Statement
Hypothesis Generation
Loading Packages and Data
Understanding the Data
Univariate Analysis
Bivariate Analysis
Missing Value Treatment
Feature Engineering
Encoding Categorical Variables
PreProcessing Data
Model Building
Linear Regression
Regularized Linear Regression
Random Forest
XGBoost
Summary
AI&ML Blackbelt Plus Program (Sponsored)
Enroll for free
FAQs
Who should take this course?
Do I need to install any software before starting the course?
What is the refund policy?
Do I need to take the modules in a specific order?
Do I get certificate upon completion of the course?
What is the fee for this course?
How long I can access the course?
Is there any placement support?
Course Name : Twitter Sentiment Analysis || Course Description : Twitter Sentiment Analysis
What is sentiment analysis? Why is sentiment analysis so popular in data science? And how can you perform sentiment analysis? Find the answers to all these questions in this free course on Sentiment Analysis using Python!
Enroll for free
4.7/5
Intermediate
What is Sentiment Analysis?
Sentiment Analysis or Opinion Mining is a technique used to analyse the emotion in a text. We can extract the attitude or the opinion of a piece of text and get insights on it.
In the context of machine learning, you can think of Sentiment Analysis as a Classification problem where the text can either have a positive sentiment, a negative sentiment or a neutral one.

What are the applications of Sentiment Analysis in the industry?
In the age of social media, it is extremely common to comment about
a movie you liked or
a book you didn’t like or
a product you bought was not up to the mark.
Therefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customer’s opinion.
It is also important to detect and remove hateful content from social media and companies like Twitter, Facebook, etc. extensively use sentiment analysis on a daily basis.
On what kind of projects would I implement sentiment analysis?
There are a wide variety of projects where you can use Sentiment Analysis. Here are a couple of popular use cases:
Sentiment Analysis can not only be used for customer reviews or product feedback, but in other domains as well.
Analyzing the sentiments on social media on the US Elections, for example, gives useful insights on which candidates are favoured by the public and which are not.
For other interesting problems involving sentiment/emotion detection, you can visit: https://datahack.analyticsvidhya.com/contest/all/

What is the range of sentiments that can be observed and analysed?
In the earlier days of Natural language processing and Sentiment Analysis, the sentiments could hold only 2 or 3 values: Positive or Negative, and Positive, Neutral or Negative.
However, with the advent of deep learning, we can now recognize the subtle emotions in a text as well.
This has made tasks like Sarcasm detection, fake news detection etc. popular in research areas of Natural language processing

Can I add this project to my resume and use it in my Interview?
Sentiment Analysis is one of the most popular applications of Machine Learning and Classification in Natural language processing
We also encourage you to take up more diverse datasets and apply sentiment analysis on them.
Sentiment Analysis is also one of the first projects you would learn in your Natural language processing journey and as such is commonly asked in interviews.
Course curriculum
1
Twitter Sentiment Analysis (Using Python)
Overview of the Course
Understand the Problem Statement
Table of Contents
Loading Libraries and Data
Data Inspection
Data Cleaning
Story Generation and Visualization from Tweets
Bag-of-Words Features
TF-IDF Features
Word2Vec Features
Modeling
Logistic Regression
Support Vector Machine (SVM)
RandomForest
XGBoost
FineTuning XGBoost + Word2Vec
Summary
AI&ML Blackbelt Plus Program (Sponsored)
Enroll for free
FAQs
Who should take this course?
Do I need to install any software before starting the course?
How long would I have access to “Twitter Sentiment Analysis” course?
Do I need to take the modules in a specific order?
Do I get certificate upon completion of the course?
What is the fee for this course?
Is there any placement support?
How can I apply and test my learnings about Sentiment Analysis?
How much effort will this course take?
Course Name : Pandas for Data Analysis in Python || Course Description : Pandas for Data Analysis in Python
What is Pandas? How can you perform data analysis and data manipulation using Pandas in Python? Learn how to work with Pandas in this superb free course and master the most popular Python library in data science.
Enroll for free
Learn Pandas - The Most Popular and Useful Python Library for Data Science
Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.
According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world. That is quite an achievement!
Pandas is the first library we import when we fire up our Jupyter notebooks (‘import pandas as pd’ is indelibly etched in our minds!). It is a super flexible tool that enables us to perform data analysis and data manipulation on Pandas dataframes in double-quick time.
As a beginner in data science and especially Python, you’ll have a lot of questions about Pandas
What is Pandas?
How can I install Pandas in Python?
Where is Pandas used in data science?
How difficult is it to learn Pandas?
What is a Pandas dataframe?
Do I need to know Python to learn Pandas?
What kind of data analysis can I perform using Pandas?
Will Pandas help me become a better data scientist?
What kind of data formats can I import using Pandas?

If you’ve asked any of these questions before or are looking to learn Pandas from scratch, you’ve come to the right place.
This free course by Analytics Vidhya will introduce you to the world of Pandas in Python, how you can use Pandas to perform data analysis and data manipulation. The perfect starting course for Python and Pandas beginners!
Start now for Free
What does this Pandas course cover?
The great thing about Pandas is the sheer number of tasks you can perform in Python. It is often called the Swiss Army Knife of data analysis! That should give you a good idea of what you can expect from this powerful library.
Here’s a taste of what we’ll cover in this course:
Basics of the Pandas library
How to import data using Pandas: Read data into Python
How to write data using Pandas
Perform data analysis and manipulation using Pandas:
Select columns and rows in Pandas
Manipulate columns in Pandas - Rename columns, sort data in Pandas dataframe, binning data using Pandas, etc.
How to deal with missing values using Pandas
What is the Apply function in Pandas and how you can use it
Aggregate data in Pandas - a very handy tool for quick data analysis
How to merge and join multiple Pandas dataframes
Pivot tables in Pandas (yes, you can draw up pivot tables in Python using Pandas!)
And we have even included an illustrated Pandas cheatsheet just for you!
Who is this Pandas for Data Analysis using Python course for?
This course is designed for:
Beginners in Python who are curious about Pandas and how to use it for data analysis and data manipulation
Anyone who wants to start their data science career (Pandas is the first library you’ll import!)
Anyone looking to get into a data analyst role using Python programming
Anyone who wants to jump from Excel into Python
Course curriculum
1
Getting Started with Pandas
Introduction to the Course
Pandas Installation
AI&ML Blackbelt Plus Program (Sponsored)
2
Dataset Description
3
Read & Write Data using Pandas
4
Pandas Dataframes
5
Data Exploration using Pandas
6
Data Manipulation using Pandas
7
Aggregating data using Pandas
8
Merging Data using Pandas
9
Pandas Cheatsheet
Instructor(s)
Common Questions Beginners ask about Pandas
What is Pandas?
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation library built in Python.

Pandas is THE most popular Python library in data science and the 4th most popular library in the world (according to StackOverflow’s global survey). The open source nature of Pandas isone of the primary reasons for its popularity and adoption rate in the community.

Here’s a golden nugget about Pandas from Wikipedia: The name ‘Pandas’ is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.
How can I install Pandas in Python?
The easiest way to install Pandas is to install it as part of the Anaconda distribution. Anaconda is free and easy to install (it’s the installer we use for setting up Python as well).

We highly recommend doing this instead of trying to install Pandas from scratch (it will be a slightly difficult process if you’re not familiar with Python or programming in general).
Where is Pandas used in data science?
Pandas is primarily used in data science and machine learning in the form of dataframes. As we’ve mentioned above, Pandas enables us to perform all sorts of data analysis and manipulation tasks in Python, including importing different data files like CSV, Excel, JSON, etc.

Most data science projects use Pandas to perform aggregating functions like GroupBy, merge and join dataframes, impute missing values in Python, among other things.

In short, Pandas is an essential part of a data science project!
What is a Pandas dataframe?
In Pandas, a dataframe is a data structure used to store and manipulate tabular data. The tabular data has it’s columns, column names and rows - we can easily perform operations on large dataframes using Pandas functions.

A Pandas dataframe is also the standard structure used to store the data from common formats of data like CSV file, Excel sheets and others.
How difficult is it to learn Pandas?
It’s actually quite straightforward! Even though Pandas has a ton of features and functions, you can easily pick those up with a bit of practice.

And that’s exactly how we’ve designed the course! You’ll learn all the different Pandas functions in Python and then work on various exercises after each lesson to solidify what you’ve learned.
Do I need to know Python to learn Pandas?
It would definitely help to have basic Python programming knowledge if you want to maximize your Pandas experience. The ability to merge or join Pandas dataframes, manipulating data and so on will require a bit of Python programming.

If you’re completely new to Python, we recommend taking our free Python for Data Science course.
What kind of data analysis can I perform using Pandas?
You can perform all kinds of data analysis and data manipulation using Pandas. We cover the key points in this course but here is a list for your reference:
● Reading and writing data from different file formats like CSV, Excel, JSON, etc.
● Data alignment
● Handling missing data
● Reshaping data and building pivot tables using Pandas
● Label-based slicing, fancy indexing, and subsetting of large datasets
● Column insertion and deletion using Pandas dataframes
● Group by allowing split-apply-combine operations on datasets
● Dataset merging and joining
● Filtering dataframes, and so on
Will Pandas help me become a better data scientist?
Short answer - yes. Which data science project won’t appreciate someone who can perform quick analysis and data manipulation? This is a key part of any data science project and mastering Pandas will go a long way towards making you a better and more efficient data scientist.

Additionally, this will also help you in your interview rounds when you’re asked to analyze certain data (Pandas is your best friend!).
What kind of data formats can I import using Pandas?
The beauty about Pandas is the remarkable number of files we can read into Python. Here’s a quick list:
1. Comma-separated values (CSV)
2. XLSX
3. ZIP
4. Plain Text (txt)
5. JSON
6. XML
7. HTML
8. Images
9. Hierarchical Data Format
10. PDF
11.DOCX
12. MP3
13. MP4
14. SQL
What do I need to start with Pandas for Data Analysis in Python course?
A working laptop / desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python - check out this Course
first, if you are new to Python

This is all it takes for you to learn one of the most popular and useful library for data analysis in Python.
What are you waiting for?
Enroll for Free Now
Frequently Asked Questions
Common questions related to Pandas for Data Analysis in Python course
Who should take Pandas for Data Analysis in Python course?
I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
What is the fee for this course?
How long would I have access to "Pandas for Data Analysis in Python" course?
How much effort will this course take?
How can I apply and test my learnings about Pandas for Data Analysis?
Can I download videos from this course?
Which programming language is used to teach Pandas in this course?
Do I get a certificate upon completion of the course?
I just completed Pandas for Data Analysis in Python course, what should I do next?
I don't have Python Installed in my machine, what can I do?
How is a Free Course different from a paid course on Analytics Vidhya?
Enroll in Pandas for Data Analysis in Python today
More than 1 Million users use Analytics Vidhya every month to learn Data Science. Start your journey now!
Get started for Free
Course Name : Support Vector Machine (SVM) in Python and R || Course Description : Support Vector Machine (SVM) in Python and R
What is a Support Vector Machine (SVM) in machine learning? How does an SVM classifier work? How you can design an SVM classifier in Python and R? Learn all about Support Vector Machines (SVM) in this free course for data scientists!
Enroll for free
Learn Support Vector Machines (SVM) in Python and R
Want to learn the popular machine learning algorithm - Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.
This free course will not only teach you basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.
This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.
Here are key questions you should know the answer to:
Support Vector Machines (SVM) can be a tricky topic to learn if you aren’t asking the right questions. Here are a few key questions about SVM you should know:
What is a Support Vector Machine (SVM)?
Is SVM a must-know algorithm in machine learning?
What kind of machine learning problems can I solve using SVM?
Can I perform both classification and regression using SVM?
What do I need to know before learning SVM?
What is a SVM classifier?
Can I rely on the Support Vector Machine algorithm in a Kaggle or DataHack hackathon?
How difficult is it to learn SVM for a beginner in machine learning?
What is a SVM kernel?

If you are not sure about any of the above questions, it’s the right time to enroll in this free course and start your SVM learning!
This free course by Analytics Vidhya will provide you with a solid introduction to Support Vector Machines (SVM) and how this popular machine learning algorithm works under the hood. We will also implement SVM in Python and R to give you a practical understanding of this algorithm. A perfect course in your machine learning journey!
Enroll now
Course curriculum
1
Introduction to Support Vector Machines
What are Support Vector Machines?
Why do we use SVM and how is it better?
AI&ML Blackbelt Plus Program (Sponsored)
2
How does SVM work?
3
SVM Kernels and Hyperparameters
4
Implementing SVM in Python
5
Implementing SVM in R
6
Challenges of SVM
Instructor(s)
What does this Support Vector Machine (SVM) course cover?
Support Vector Machine (SVM) can appear as a complex topic if you’re going about it the wrong way. This course is designed in a structured manner to ensure you learn SVM in an easy to understand way. We have also included exercises and a popular machine learning project to help you gain a practical understanding of Support Vector Machines.
Here are the highlights of this Support Vector Machine (SVM) in Python and R course:
Support Vector Machine (SVM) basics
What is SVM?
What is a SVM classifier?
Why should you use SVM?(Advantages of SVM)
How does the Support Vector Machine algorithm work?
What are the different SVM hyperparameters?
What is a SVM kernel?
The different SVM kernels:
SVM linear kernel
SVM RBF kernel
Implementing SVM in Python using the sklearn.svm.svc function
Implementing SVM in R using the e1071 package
Challenges you might face while implementing SVM in machine learning

This course on Support Vector Machines (SVM) is a taste of the various machine learning algorithms out there. We recommend taking the ‘Applied Machine Learning’ course to get the full package!
Who is this Support Vector Machine (SVM) in Python and R course for?
This course is designed for:
Anyone who wants to start their machine learning career and is looking to learn the different machine learning algorithms
Anyone who wants to become a data scientist or a machine learning engineer
Anyone interested in learning how a classic machine learning algorithm like SVM works!
What do I need to start with Support Vector Machine in Python & R course?
A working laptop / desktop with 8 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python / R - check out this Course
first, if you are new to Python

This is all it takes for you to learn one of the most powerful algorithm in Machine Learning.
What are you waiting for?
Enroll for Free Now
Common Questions Beginners ask about Support Vector Machine (SVM)
What is a Support Vector Machine (SVM)?
“Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression problems. SVM is one of the most popular algorithms in machine learning and we’ve often seen interview questions related to this being asked regularly.
Is SVM a must-know algorithm in machine learning?
SVM is definitely an algorithm every data scientist or machine learning engineer should know. It is a widely adopted algorithm across organizations.

Even on a personal learning front, learning SVM and how it works will add a lot to your own machine learning knowledge.
Can I perform both classification and regression using SVM?
Yes! As we mentioned above, you can use Support Vector Machine (SVM) for both classification and regression problems. However, SVM is popularly used far more for classification tasks.

This is something you will learn inside the course as well. You will get an intuitive understanding as to why SVM gives such an accurate output when we apply it on classification problems.
What kind of machine learning problems can I solve using SVM?
Support Vector Machine can be used for a wide variety of classification problem like, text classification, default loan prediction. You can try implementing SVM on different kinds of datasets to learn it in detail (https://datahack.analyticsvidhya.com/contest/all/)
What do I need to know before learning SVM?
There are three key components you would need to know before jumping to SVM:
A working knowledge of Python (we recommend the Python for Data Science course)
If not Python, then a working knowledge of R
Basics of Machine Learning (what is supervised learning, what is classification and regression, etc.). You can take the ‘Introduction to Data Science’ course to learn all of these concepts
What is an SVM classifier?
The SVM classifier is basically a machine learning algorithm which is used for classification tasks. We mainly use it for classifying data which can’t be separated by a straight line.
Can I use Support Vector Machine algorithm in a Kaggle or DataHack hackathon?
Of course! You should always try out as many algorithms as you can in machine learning competitions. We always encourage our community to experiment on DataHack and Kaggle projects and hackathons.

You can apply what you learn in this course on Kaggle and DataHack hackathons for sure.
How difficult is it to learn SVM for a beginner in machine learning?
SVM can be tricky….if you aren’t paying attention. If you follow the modules and lessons in this course, you’ll be a Support Vector Machine expert in a few hours!

Remember - practice is key. The more you practice your newly acquired SVM knowledge, the better you will become. Apply your learning on the various classification projects on the DataHack platform.
What is a SVM kernel?
Since SVM is primarily used to classify non-linearly separable data, it provides a variety of functions to segregate the classes. These functions are called kernels. You can study more about these functions in the course and observe how SVM works.
Project - Iris Species Classification
Use Support Vector Machines algorithms to classify species of Iris flowers
You will analyse what features of an Iris flower can be used to classify its species for e.g. petal length, sepal length etc. You will build a classifier using Support Vector Machines (SVM)
Frequently Asked Questions
Common questions related to Support Vector Machines in Python & R course
Who should take Support Vector Machines in Python & R course?
I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
What is the fee for this course?
How long would I have access to "Support Vector Machines in Python and R" course?
How much effort will this course take?
How can I apply and test my learnings about Support Vector Machines?
Can I download videos from this course?
Which programming language is used to teach Support Vector Machines in this course?
Do I get a certificate upon completion of the course?
I just completed Support Vector Machine course, what should I do next?
I don't have Python Installed in my machine, what can I do?
How is a Free Course different from a paid course on Analytics Vidhya?
Can I add this project on my resume and use it in my interview?
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Course Name : Evaluation Metrics for Machine Learning Models || Course Description : Evaluation Metrics for Machine Learning Models
What are evaluation metrics in machine learning? What are the different types of evaluation metrics? How do you gauge and improve your machine learning model? This course will teach you all about evaluation metrics for machine learning models!
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Evaluation Metrics are a Key Part of Machine Learning Models
Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores - unable to understand which model is performing well.
Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:
We build a machine learning model (both regression and classification included)
Get feedback from the evaluation metric(s)
Make improvements to the model
Use the evaluation metric to gauge the model’s performance, and
Continue until you achieve a desirable accuracy
Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.
If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well - you’ve come to the right course!
As a Machine Learning and Data Science aspirant, you need to be able to answer these questions about evaluation metrics:
What is an evaluation metric?
What are the different types of evaluation metrics?
Do I really need to master evaluation metrics to understand machine learning?
What kind of evaluation metrics questions can I expect in an interview?
How do evaluation metrics help me improve my hackathon rankings?
Can I use the same evaluation metrics for regression as well as classification problems?
Is cross validation an evaluation metric?
You will learn all about evaluation metrics for machine learning models in this course. We will discuss the different types of evaluation metrics, including how to use them for evaluating classification and regression models.
Enroll for free
When to use Evaluation Metrics in Machine Learning?
We have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their machine learning model is. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach.
Simply building a machine learning model is not the motive! It’s about creating and selecting a model which gives high accuracy on an out of sample data (or unseen data). Hence, it is crucial to check the accuracy of your model prior to computing predicted values.
This is where evaluation metrics help us.
Course curriculum
1
Introduction
Types of Machine Learning
Why do we need Evaluation Metrics?
AI&ML Blackbelt Plus Program (Sponsored)
2
Evaluation Metrics: Classification
3
Evaluation Metrics: Regression
4
What Next?
Common Questions Beginners Ask About Evaluation Metrics
What is an evaluation metric?
The answer lies in the name itself! Evaluation metrics help us evaluate, or gauge, the performance (or accuracy) of our machine learning model. They are an integral part of the machine learning model building pipeline as we can iterate and improve our model’s performance by judging how it’s working.
What are the different types of evaluation metrics?
There are several types of evaluation metrics for machine learning models. The choice of the evaluation metric completely depends on the type of model and the implementation plan of the model.
The evaluation metrics varies according to the problem types - whether you’re building a regression model (continuous target variable) or a classification model (discrete target variable).
In this course, we’re covering evaluation metrics for both machine learning models. Here’s a taste of the different evaluation metrics you’ll find in the course:
Confusion Matrix (Classification evaluation metric)
F1 Score (Classification evaluation metric)
AUC-ROC (Classification evaluation metric)
Gini Coefficient (Classification evaluation metric)
Root Mean Squared Error (RMSE - Regression evaluation metric), and many more!
Do I really need to master evaluation metrics to understand machine learning?
The short answer - yes. Evaluation metrics are critical to judging and improving our machine learning model’s performance. And who doesn’t want to do that?
Evaluation metrics are a must-know concept for every machine learning and data science aspirant.
What kind of evaluation metrics questions can I expect in an interview?
Here are a few common questions we’ve seen asked of beginners about evaluation metrics:
What are the different types of evaluation metrics for regression and classification problems?
Given a particular classification problem, should you use AUC-ROC or F1 Score (or something else)?
What is precision and recall? How does that help in evaluating your machine learning model?
What should you do if an evaluation metric is not working according to what you expected?
You’ll get a better idea about how to answer these questions inside the course.
How do evaluation metrics help me improve my hackathon rankings?
Evaluation metrics, as you might have guessed by now, will be of supreme importance in machine learning hackathons. Your ranking on the hackathon leaderboard will be based on the evaluation metric being used in that hackathon.
There’s no getting away from it - evaluation metrics are the lifeblood of your machine learning model’s performance.
Can I use the same evaluation metrics for regression as well as classification problems?
Not quite. Regression and classification models have their separate evaluation metrics. Remember , the evaluation metric depends on the target variable. If your target variable is continuous (aka a regression problem), you can’t use a classification metric to evaluate it!
Is cross validation an evaluation metric?
Cross validation is not technically an evaluation metric but we’ve still included this in the course. Cross Validation is one of the most important concepts in any type of machine learning model and a data scientist should be well versed in how it works.
FAQ
Common questions related to the Evaluation Metrics for Machine Learning Models course
Who should take the Evaluation Metrics for Machine Learning Models course?
This course is designed for anyone who wants to learn how to evaluate their machine learning models. So if you’re a newcomer to machine learning and want to improve your model’s performance, this course is for you!
I have decent programming experience but no background in machine learning. Is this course right for me?
Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Evaluation Metrics for Machine Learning Models” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Evaluation Metrics for Machine Learning Models” course in a few hours.
I’ve completed this course and have decent knowledge about evaluating machine learning models. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course to understand the end-to-end machine learning pipeline, and how evaluation metrics play a part there.
Can I download the videos in this course?
Enroll in Evaluation Metrics for Machine Learning Models today
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Course Name : Fundamentals of Regression Analysis || Course Description : Fundamentals of Regression Analysis
What is regression analysis? What are the different types of regression? What’s the difference between linear regression, logistic regression, ridge and lasso regression? This course on fundamentals of regression analysis will clear all your doubts!
Enroll for free
Learn all about Regression Analysis and the Different Types of Regression
Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.
Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.
The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:
Ridge regression
Lasso regression
Polynomial regression
Stepwise regression, among others.
Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.
Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.
As a beginner with Regression Analysis, you’ll have a lot of questions:
What is Regression Analysis?
When should you use Regression?
How many types of Regressions do we have?
How much mathematical knowledge is required to understand regression?
Ridge vs. Lasso Regression - what’s the difference?
Which types of problems can be solved using regression?
What are the major challenges faced by regression techniques?
Is Regression Analysis relevant in the industry?
Which programming language works best for regression?
What kind of machine learning projects can you do using regression techniques?
In this free course, you will build a solid understanding of what regression is and the different types of regression techniques out there. You will also get a taste of how regression analysis works, the different assumptions you would need to make in regression analysis, and yes, we will answer the linear regression vs logistic regression question!
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Fundamentals of Regression Analysis Course Curriculum
1
Welcome to the course!
Welcome!
2
Introduction to Regression
3
Types of Regression
4
Linear Regression
5
Logistic Regression
6
Ridge Regression
7
Lasso Regression
8
Selecting the Right Model
9
What next?
Instructor(s)
Common Questions Beginners Ask about Regression Analysis
What is Regression Analysis?

Regression Analysis is one of the most widely and popularly used techniques of analyzing data. Almost in all the data science courses that exist, regression is one of the first machine learning algorithms to be taught.
It is used when there is a cause and effect relationship between the dependent variable (target) and independent variable/variables (predictors).
How many types of Regression do we have?

There are multiple types of regression techniques for making predictions. Apart from linear regression, here are a few others that are commonly used in the industry and in research:
Ridge regression
Lasso regression
Polynomial regression
Stepwise Regression
ElasticNet Regression, among others.
How much mathematical knowledge is required to understand regression?

The maths behind regression analysis is simple and easy to understand. The knowledge of maths includes:
Probability
Partial Derivation
Linear Algebra
Statistics
Don’t worry! We will cover the core concepts in the course itself.
Ridge vs. Lasso Regression - what’s the difference?

In mathematical terms, Ridge penalises the loss function by adding the squared value of coefficients whereas Lasso Regression penalises the loss function by adding the absolute value of the coefficient of the variable.
You’ll find out more about each regression in the course.
Which types of problems can be solved using regression?

Any problem having a cause and effect relationship can be solved by regression analysis. Regression techniques help you solve both linear and classification problems. Some practical implementations include:
Predicting prices of a commodity
Predicting demand of a commodity
Predicting binary outcomes such as Credit Default
Predicting multi-class problems such as Genre of Movie, etc.
What are the major challenges faced by regression techniques?

Some of the problems faced by regression techniques include-
Multicollinearity - A situation where the predictor variables are correlated with one another.
Correlation of error terms - This is when the error terms form a pattern when plotted in the graph.
Underfitting/Overfitting - If there is an abundance of predictor variables the regression model might overfit and if there is not enough data it will lead to underfitting.
Is Regression Analysis relevant in the industry?

Absolutely! Regression analysis is one of the most commonly used methods in analytics, statistics, and data science projects. Despite the incredible number of breakthroughs in machine learning and the plethora of other algorithms out there, linear regression remains the most popular technique in a lot of organizations.
Which programming language works best for regression?

Here’s the beauty of regression analysis - you can use any tool or programming language to build regression models. You can perform regression analysis in MS Excel, R, Python, Minitab, KNIME - the list goes on and on.
We have used Python to implement the different regression types in this course.
What kind of machine learning projects can you do using regression techniques?

You can pick up almost any regression problem out there and use the techniques you learn in the course. We suggest heading over to Analytics Vidhya’s DataHack platform and picking up the problem or project you can relate to.
FAQ
Common questions related to the Fundamentals of Regression Analysis course
Who should take the Fundamentals of Regression Analysis course?
This course is aimed towards beginners in data science, machine learning and even statistics. Regression analysis and the different forms of regression like linear regression are key concepts in these fields. We have designed the course such that even newcomers will be able to follow along easily and be able to build regression models by the end of the course!
I have some programming experience but no background in machine learning or statistics. Is this course right for me?
Absolutely! The Fundamentals of Regression Analysis course is easy to follow along and we have provided the appropriate resources where necessary throughout the course.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Fundamentals of Regression Analysis” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Fundamentals of Regression Analysis” course in a few hours. You are also expected to apply your knowledge of the different regression types and learning of this course to solve machine learning problems. The time taken in projects varies from person to person.
I’ve completed this course and have decent knowledge about Regression Analysis, linear regression, and logistic regression. What should I learn next?
That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course. That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course!
Can I download the videos in this course?
We regularly update the “Fundamentals of Regression Analysis” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Which programming language is used to teach the Introduction to Fundamentals of Regression Analysis course?
Enroll in Fundamentals of Regression Analysis today
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Course Name : Getting Started with scikit-learn (sklearn) for Machine Learning || Course Description : Getting Started with scikit-learn (sklearn) for Machine Learning
Scikit-learn (Sklearn) - the powerful Python library for machine learning. But what is sklearn? How does sklearn work? What kind of ML models can you build using sklearn? Find out in this course and build sklearn models!
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Learn All About sklearn - The Powerful Python Library for Machine Learning
Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:
Linear Regression using sklearn
Logistic Regression using sklearn, and so on.
There’s no question - scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.
We love the clean, uniform code and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.
In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree or a random forest, sklearn is your go-to library.
New to Machine Learning and sklearn? Here are a few key questions you will encounter in your journey:
What is scikit-learn or sklearn?
What’s the difference between scikit-learn and sklearn?
How do I install sklearn?
Do I need to know machine learning to use sklearn?
What kind of machine learning models can I build using sklearn?
How much programming knowledge do I need to have to master sklearn?
Which programming language should I know for sklearn?
What are the different functions or areas that sklearn covers?
Which machine learning focused organization are using sklearn?
What kind of projects can I work on using sklearn?

This free course by Analytics Vidhya will teach you all you need to get started with scikit-learn for machine learning. We will go through the various components of sklearn, how to use sklearn in Python, and of course, we will build machine learning models like linear regression, logistic regression and decision tree using sklearn!
Who is the Getting Started with scikit-learn (sklearn) course for?
Scikit-learn is THE go-to Python library for building machine learning models. So if you’re in any of the below roles/phases, this course is for you!
Machine learning aspirant
Machine learning fresher
Data science enthusiast
Team leader
In a senior role for a machine learning project
Just want to understand how machine learning models are designed
Prerequisites for the Getting Started with scikit-learn (sklearn) course
You don’t need to be a machine learning master to get started with sklearn! There are primarily two prerequisites:
Basic machine learning knowledge: It would help if you knew the different machine learning models, such as linear regression and logistic regression. This will help you work with sklearn in a much more efficient manner
Basic Python knowledge: You should ideally know the basics of Python to be able to work with sklearn. We recommend the incredibly popular (and free) ‘Python for Data Science’ course to get your feet wet
This is where you seal the deal. Sprinkle this section throughout your page to push prospects to purchase!
Enroll for free now
Course curriculum
1
Welcome to the course!
Welcome to this course
2
scikit-learn in Python
3
Use of Scikit-learn in Data Science Life Cycle
4
Use of Scikit-Learn in Model Building
5
Machine Learning pipeline using scikit-learn!
6
Next Steps...
Common Questions Machine Learning Beginners Ask About Scikit-learn (sklearn)
What is scikit-learn or sklearn?
Sklearn, short for scikit-learn, is a Python library for building machine learning models. Sklearn is among the most popular open-source machine learning libraries in the world.
Scikit-learn is being used by organizations across the globe, including the likes of Spotify, JP Morgan, Booking.com, Evernote, and many more.

What’s the difference between scikit-learn and sklearn?
Scikit-learn and sklearn are one and the same! This Python library is popularly known as sklearn because that’s how you use it when working in Python.
You’ll soon be very familiar with commands that include “from sklearn import….”!
How do I install sklearn?
Sklearn comes with the ANaconda distribution by default. So if you already have Python in your machine, you’ll have sklearn inbuilt. However, that might not be the latest version.
There are a couple of other ways to install scikit-learn:
Install the latest official release from the sklearn website
Building the package from source. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code
Do I need to know machine learning to use sklearn?
Basic machine learning knowledge will definitely help. Sklearn helps us build machine learning models in Python but we need to know which model we want to build, how to tune that model, and how to evaluate it.
For example, let’s say you want to perform linear regression using sklearn. You would need to know what linear regression is, right? Also, it would help you to understand how to improve your linear regression model’s accuracy once you run it. All of these things will help you build better machine learning models using the sklearn library.
What kind of machine learning models can I build using sklearn?
Good question! Here’s where sklearn really shines. You can build all sorts of machine learning models using sklearn, for both supervised and unsupervised learning.
Here’s a broad list of machine learning models you can build using sklearn:
Linear Regression
Logistic Regression
Decision Trees
Random Forest
Support Vector Machine (SVM)
Naive Bayes
K-means Clustering
k-Nearest Neighbor, among many others!
How much programming knowledge do I need to have to master sklearn?
You should know basic Python. That’s it. Familiarity with Pandas and NumPy will of course be hugely beneficial.
We recommend taking Analytics Vidhya’s free ‘Python for Data Science’ course if you want to learn Python. That course will teach you all you need to know about Python from scratch and set you up perfectly for the ‘Getting Started with sklearn’ course.
Which programming language should I know for sklearn?
You’ll know the answer to this by now! Python is the programming language you will be working with for building machine learning models using sklearn.
What are the different functions or areas that sklearn covers?
Scikit-learn has reorganized and restructured its functions & packages into six main modules:
Classification: Identifying which category an object belongs to
Regression: Predicting a continuous-valued attribute associated with an object
Clustering: For grouping unlabeled data
Dimensionality Reduction: Reducing the number of random variables to consider
Model Selection: Comparing, validating and choosing parameters and models
Preprocessing: Feature extraction and normalization
Which machine learning focused organization are using sklearn?
Pretty much every machine learning focused organization has leveraged sklearn. Here are a few top organizations:
J.P. Morgan
Spotify
Hugging Face
Evernote
Booking.com
Yhat
DataRobot
You can view the full list on sklearn’s official documentation page.
What kind of projects can I work on using sklearn?
You can work on both supervised learning and unsupervised learning projects using sklearn. Under supervised, sklearn can be applied on both regression as well as classification problems.
So whether it’s a simple linear regression model, or a complex ensemble learning technique, sklearn is your library! We suggest going to our DataHack platform, picking up a problem you want, and applying what you’ve learned in this course.
FAQ
Common questions related to the Getting Started with scikit-learn (sklearn) for Machine Learning course
Who should take the Getting Started with scikit-learn (sklearn) for Machine Learning course?
This course is designed for anyone who wants to get started with machine learning. So whether you’re a machine learning aspirant who is just starting out or a team leader looking to understand how it all works, this course is for you.
I have some programming experience but no background in machine learning. Is this course right for me?
Sure. You will be able to follow along with the Python code but it’ll definitely help if you know basic machine learning algorithms, like linear regression, logistic regression, and decision trees.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Getting Started with scikit-learn (sklearn) for Machine Learning” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Getting Started with scikit-learn (sklearn) for Machine Learning” course in a few hours. You are also expected to apply your knowledge and learning of this course to solve machine learning problems. The time taken in projects varies from person to person.
I’ve completed this course and have decent knowledge about sklearn and the different machine learning algorithms. What should I learn next?
That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course. That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course!
Can I download the videos in this course?
We regularly update the “Getting Started with scikit-learn (sklearn) for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Which programming language is used to teach the Getting Started with scikit-learn (sklearn) for Machine Learning course?
This course uses Python programming language throughout
Enroll in Getting Started with scikit-learn (sklearn) for Machine Learning today
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Course Name : Convolutional Neural Networks (CNN) from Scratch || Course Description : Convolutional Neural Networks (CNN) from Scratch
Convolutional neural networks, or CNNs, have taken the deep learning community by storm. These CNN models power deep learning applications like object detection, image segmentation, facial recognition, etc. Learn all about CNN in this course.
Enroll for free
4.7/5
Advanced
Learn about Convolutional Neural Networks (CNN) from Scratch
Convolutional Neural Networks, or CNN as they’re popularly called, are the go-to deep learning architecture for computer vision tasks, such as object detection, image segmentation, facial recognition, among others. CNNs have even been extended to the field of video analysis!
If you are picking one deep learning architecture to learn and are not sure where to start, you should go for convolutional neural networks. Deep learning enthusiasts and experts with CNN knowledge are being widely sourced in the industry.
It’s your time to use this CNN skillset and shine!
Who is the Convolutional Neural Network (CNN) from Scratch Course For?
This course is designed for anyone who is:
Interested in learning about CNNs
A newcomer to deep learning
Exploring the various aspects of deep learning
Curious about the most popular type of neural network for working with image data!
You can go through the Introduction to Neural Networks course first.
What do you need to get started with the CNN course?
Here’s what you’ll need:
8GB of RAM
i5 processor
1TB of storage
4 GB of Nvidia Graphics Card
Course curriculum
1
Introduction to Neural Networks
What is a Neural Network?
Types of Neural Networks
Prerequisites
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction to CNNs
3
Architecture of a CNN
4
Mathematics behind CNNs
5
Implementing a CNN
6
What Next?
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
FAQs
Who should take the Convolutional Neural Networks (CNN) from Scratch course?
I have decent programming experience but no background in deep learning. Is this course right for me?
What is the fee for the course?
How long would I have access to the “Convolutional Neural Networks (CNN) from Scratch” course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next?
Can I download the videos in this course?
Course Name : Dimensionality Reduction for Machine Learning || Course Description : Dimensionality Reduction for Machine Learning
Dimensionality reduction is a key concept in machine learning. This course covers several dimensionality reduction techniques that every data scientist should know, including Principal Component Analysis (PCA) and Factor Analysis, among others!
Enroll for free
Learn All About the Power of Dimensionality Reduction
Have you worked on a dataset with more than a thousand features? How about 40,000 features? We are generating data at an unprecedented pace right now and working with massive datasets in machine learning projects is becoming mainstream.
This is where the power of dimensionality reduction techniques comes to the fore. Dimensionality reduction is actually one of the most crucial aspects in machine learning projects.
You can use dimensionality reduction techniques to reduce the number of features in your dataset without having to lose much information and keep (or improve) the model’s performance. It’s a really powerful way to deal with huge datasets, as you’ll see in this course!
Every data scientist, aspiring established, should be aware of the different dimensionality reduction techniques, such as Principal Component Analysis (PCA), Factor Analysis, t-SNE, High Correlation Filter, Missing Value Ratio, among others.
So in this beginner-friendly course, you will learn the basics of dimensionality reduction and why you should know dimensionality reduction in machine learning. We will also cover 12 dimensionality reduction techniques! This course is as comprehensive an introduction as you can get!

Enroll for free
You should know the answer to the below questions on dimensionality reduction:
What is dimensionality reduction?
What are the different dimensionality reduction techniques?
Why should I learn dimensionality reduction?
I already know what Principal Component Analysis (PCA) is. Do I really need to learn more dimensionality reduction techniques?
Do I need to have huge computational power to apply dimensionality reduction?
What are the different applications of dimensionality reduction?
What kind of machine learning projects can I apply dimensionality reduction on?
Will learning about dimensionality reduction and techniques like PCA help me in clearing machine learning interviews?
Is dimensionality reduction a supervised or unsupervised machine learning technique?
What are the challenges of applying dimensionality reduction techniques?

Who is the Dimensionality Reduction for Machine Learning Course for?
This dimensionality reduction course is designed for machine learning folks who:
Want to understand how to work with high dimensional data
Are struggling to build machine learning models on a dataset with hundreds and thousands of features
Want to explore the various dimensionality reduction techniques out there
Are preparing for their machine learning journey
Want to understand where dimensionality reduction fits in
Course curriculum
1
Introduction to the Course
Introduction
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction to Dimensionality Reduction
3
Feature Selection Techniques
4
Factor Based Feature Extraction Techniques
5
Projection Based Feature Extraction Techniques
Common Questions Beginners Ask About Dimensionality Reduction for Machine Learning
What is dimensionality reduction?
Dimension Reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions ensuring that it conveys similar information concisely. These dimensionality reduction techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.
What are the different dimensionality reduction techniques?
There are multiple dimensionality reduction techniques for machine learning. We will cover 12 such techniques in this course:
Missing Value Ratio
Low Variance Filter
High Correlation Filter
Random Forest
Backward Feature Elimination
Forward Feature Selection
Factor Analysis
Principal Component Analysis (PCA)
Independent Component Analysis
Methods Based on Projections
t-Distributed Stochastic Neighbor Embedding (t-SNE)
UMAP
Why should I learn dimensionality reduction?
That’s a fair question! Here are a few key reasons why every machine learning professional should know dimensionality reduction:
Space required to store the data is reduced as the number of dimensions comes down
Less dimensions lead to less computation/training time
Some algorithms do not perform well when we have large dimensions. So reducing these dimensions needs to happen for the algorithm to be useful
Dimensionality reduction also takes care of multicollinearity by removing redundant features.
We can visualize high dimensional data thanks to the various dimensionality reduction techniques such as t-SNE
We could go on, but you get the point! Dimensionality reduction is a crucial cog in the machine learning project lifecycle.
I already know what Principal Component Analysis (PCA) is. Do I really need to learn more dimensionality reduction techniques?
Absolutely. Principal Component Analysis (PCA) is a powerful dimensionality reduction technique but it does have its challenges. You should consider this course as a Siwss Army Knife in your dimensionality reduction skill set!
Learning the various dimensionality reduction techniques will help you become a better machine learning practitioner and expand your horizons when you’re working on different machine learning problems.
Do I need to have huge computational power to apply dimensionality reduction?
The question on computational power varies from the volume of data we have. For a dataset containing millions of records and thousands of dimensions one needs to have huge computational power to reduce the dimensions and make the data suitable for building models.
Where as if the dataset contains only less records and dimensions the computational power required will be very less. It all depends on the volume of data and how quickly you want the result.
What are the different applications of dimensionality reduction?
There are a plethora of applications of dimensionality reduction. Here’s just 3 of them:
If the dataset has too many missing values, we use dimensionality reduction techniques to reduce the number of variables
We can find the importance of each feature and keep the top most features, resulting in dimensionality reduction
We can use dimensionality reduction to find highly correlated features and drop them accordingly
There are a whole lot more as you’ll see inside the course.
What kind of machine learning projects can I apply dimensionality reduction on?
You can apply dimensionality reduction techniques on any dataset with a ton of variables. That does not mean you should do it without considering the challenges (more on that later)!
We suggest heading over to the DataHack platform and picking up a project to apply these dimensionality reduction techniques on. After all, practice makes perfect!
Will learning about dimensionality reduction and techniques like PCA help me in clearing machine learning interviews?
Of course! As a newcomer or fresher in machine learning, you’ll be asked about how you would deal with massive datasets. It’s a common interview question and your knowledge about dimensionality reduction techniques like PCA and Factor Analysis will hold you in good stead.
Is dimensionality reduction a supervised or unsupervised machine learning technique?
Dimensionality reduction can be both supervised and unsupervised. The unsupervised techniques of Dimensionality Reduction is used when the dataset you have is humungous and is done prior to supervised dimensionality reduction technique. They include-
Principal Component Analysis
Random Projections
Feature Agglomeration
Supervised Dimensionality reduction techniques include methods like-
Linear Discriminant Analysis

What are the challenges of applying dimensionality reduction techniques?
The major challenges of applying dimensionality reduction includes-
Choosing the right variables from all the predictors is one of the most challenging tasks of dimensionality reduction. Wrong choice will lead to building a poor performing model.
The second challenge is the choice of dimensionality reduction techniques. Out of all the techniques that exist, dimensions of a dataset can be reduced using multiple dimensionality reduction technique and choosing the right one is really important to get the perfect analysis of data.
Ensuring possession of the right kind of system with appropriate computational power.
FAQ
Common questions related to the Dimensionality Reduction for Machine Learning course
Who should take the Dimensionality Reduction for Machine Learning course?
This course is designed for anyone who wants to learn about the different dimensionality reduction techniques, such as PCA and Factor Analysis. So if you’re a newcomer to machine learning and want to understand how to work with a dataset containing multiple features, this course is for you!
I have decent programming experience but no background in machine learning. Is this course right for me?
Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Dimensionality Reduction for Machine Learning” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Dimensionality Reduction for Machine Learning” course in a few hours.
I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course to understand the end-to-end machine learning pipeline, and how to use dimensionality reduction when working with massive datasets.
Can I download the videos in this course?
We regularly update the “Dimensionality Reduction for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Enroll in Dimensionality Reduction for Machine Learning
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Course Name : K-Nearest Neighbors (KNN) Algorithm in Python and R || Course Description : K-Nearest Neighbors (KNN) Algorithm in Python and R
A practical hands-on tutorial on the K-Nearest Neighbor (KNN) algorithm in both Python and R. This course covers everything you want to learn about KNN, including understanding how the KNN algorithm works and how to implement it.
Enroll for free
Learn all about the K-Nearest Neighbor (KNN) Algorithm in Machine Learning
K-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.
And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.
The KNN algorithm is simple to understand, easy to explain and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.
Before you sit for Machine Learning interviews, you should know the answers to these KNN questions:
What is the K-Nearest Neighbor (KNN) algorithm?
Why should you learn KNN in machine learning?
When should you use KNN?
Can I use KNN for both classification and regression problems?
What kind of projects can you do using KNN?
Do I need to learn Python or R (or any other programming language) to use KNN?
Will machine learning project interviews ask you about KNN?
What does the”K” in KNN stand for?
How to calculate the distance between points in KNN?

This free course by Analytics Vidhya will help you understand what K-Nearest Neighbor (KNN) is, how the KNN algorithm works, and where KNN fits in the machine learning umbrella. We will also showcase how to implement KNN in Python and R. There’s a lot to learn so enroll and get started!
Enroll now
Who is the K-Nearest Neighbor (KNN) Algorithm in Python and R Course for?
This course is for anyone who:
Wants to learn about the popular KNN algorithm and how it works
Wants to get started with machine learning algorithm
Is curious about the different algorithms in machine learning
What do you need to get started with the KNN Algorithm in Python and R course?
Here’s what you’ll need:
A working laptop/desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python / R - check out this Course first, if you are new to Python
Common Questions Beginners Ask About the KNN Algorithm
What is the K-Nearest Neighbor (KNN) algorithm?

K-Nearest Neighbor (KNN) algorithm is a distance based supervised learning algorithm that is used for solving classification problems. In this, we will be looking at the classes of the k nearest neighbors to a new point and assign it the class to which the majority of k neighbours belong too.
To identify the nearest neighbors we use various techniques of measuring distance, the most common of them being the ‘Euclidean Distance’.

Why should you learn KNN in machine learning?

KNN is among the most widely used and popular machine learning algorithms in the industry. Every data scientist, amateur or established, is expected to know the ins and outs of KNN. You should be able to implement it in Python or R (or any other programming language) if you want to land a role in the machine learning space.
Additionally, as you’ll see in the course, KNN unlocks a lot of avenues to solving machine learning problems.

When should you use KNN?

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry.

Can I use KNN for both classification and regression problems?

Yes! We just answered the question above but let’s expand a bit more on that. To evaluate any technique we generally look at 3 important aspects:
Ease to interpret output
Calculation time
Predictive Power

Let us take a few examples to place KNN on the machine learning scale:
KNN algorithm fairs well across all parameters of considerations. It is commonly used for its ease of interpretation and low calculation time.

What kind of projects can you do using KNN?

You can perform both classification and regression projects. From predicting the price of a house (regression) to identifying if a loan will default or not (classification), you can apply KNN across a range of problems.
We suggest heading to the DataHack platform, picking up a problem of your choice, and applying KNN there to solidify your practical understanding.

Do I need to learn Python or R (or any other programming language) to use KNN?

Well - yes and no. While KNN can still be performed on GUI based tools like KNIME, you should still have a working knowledge of how to implement it in Python or R. That’s a key consideration in the machine learning space.

Will machine learning project interviews ask you about KNN?

There’s a high chance you’ll be asked at least a couple of questions on the KNN algorithm. Depending on the level you’re applying for, you could be asked to explain how KNN works in a certain business scenario and how you would explain the results of a KNN model to a non-technical audience (a very common question!).

What does the ”K” in KNN stand for?

“K” in “KNN” stands for the number of cases that are considered to be "nearest" when you convert the cases as points in a euclidean space.
That means, if we consider k=3, and when a new point has to be assigned a class, it will be on the basis of the classes of the 3 nearest points that surround the new point as per the euclidean distance.

How to calculate the distance between points in KNN?

There are various methods for calculating this distance, of which the most commonly known methods are – Euclidian, Manhattan (for continuous) and Hamming distance (for categorical). While other methods do exist, these are typically the most common ones and you’ll learn more about them in the course.
Course curriculum
1
Introduction
Welcome to the Course
2
K-NEAREST NEIGHBOUR
3
Steps to Build a K-NEAREST NEIGHBOUR Model
4
Implementation in Python and R
5
What's Next?
Instructor(s)
FAQ
Common questions related to the K-Nearest Neighbor (KNN) Algorithm in Python and R course
Who should take the K-Nearest Neighbor (KNN) Algorithm in Python and R course?
This course is designed for anyone who wants to understand what the KNN algorithm is and how it works in machine learning. This is an important concept in machine learning that’s widely used in the industry. All the more reason to start learning today!
I have decent programming experience but no background in machine learning. Is this course right for me?
You should ideally have a basic grasp on machine learning algorithms and know the difference between regression and classification. We suggest enrolling in the Introduction to Data Science course for starters.
What is the fee for the course?
This course is free of cost! All you need to do is sign up and get started.
How long would I have access to the “K-Nearest Neighbor (KNN) Algorithm in Python and R” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “K-Nearest Neighbor (KNN) Algorithm in Python and R” course in a few hours.
I’ve completed this course and have a good grasp on the KNN algorithm. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.
Can I download the videos in this course?
Enroll in K-Nearest Neighbors (KNN) Algorithm in Python and R
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Course Name : Ensemble Learning and Ensemble Learning Techniques || Course Description : Ensemble Learning and Ensemble Learning Techniques
Ensemble learning is a powerful machine learning technique every data scientist should know. But what is ensemble learning? How does ensemble learning work? This course is the perfect starting point to learn all about ensemble learning.
Enroll for free
A Comprehensive Course on Ensemble Learning
Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models.

You must have used or come across several of these ensemble learning techniques in your machine learning journey:
- Bagging
- Boosting
- Stacking
- Blending, etc.

These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!
As a newcomer to Ensemble Learning in Machine Learning, you would need to know the below questions:
- What is Ensemble Learning?
- Why should you learn Ensemble Learning?
- What are the different types of Ensemble Learning techniques?
- Can you use Ensemble Learning for both Regression and Classification problems?
- What are the most popular Ensemble Learning techniques?
- What’s the intuition behind Bagging in Ensemble Learning?
- Similarly, what’s the idea behind Boosting in Ensemble Learning?
- What’s the difference between Bagging and Boosting?
- Do these Ensemble Learning techniques improve our machine learning model?
- Will learning Ensemble Learning help me crack machine learning interviews and win hackathons?
This course by Analytics Vidhya will introduce you to the concept of ensemble learning and understand the machine learning algorithms that use Ensemble Learning. To cement your understanding of this diverse topic, we will explain the advanced Ensemble Learning techniques in Python using a hands-on case study on a real-life problem!

What do you need to get started with the Ensemble Learning and Ensemble Learning Techniques course?
A working laptop/desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python / R - check out this Course first, if you are new to Python
Common Questions Beginners Ask About Ensemble Learning What is Ensemble Learning?
Just like you come to a decision to buy a car by reading multiple reviews and opinions, in machine learning also, you can combine the decisions from multiple models to improve the overall performance. This technique of combining multiple machine learning models is called ensemble learning.
Why do we need to know about Ensemble Learning?
Ensemble learning is one of the most effective ways to build an efficient machine learning model. You can build an ensemble machine learning model using simple models and yet get great scores which are at par with the resource-hungry models like neural networks.
What are the different types of Ensemble Learning techniques?
There are simple and advanced ensemble learning techniques.
Simple:
Max Voting
Averaging
Weighted Averaging
Advanced
Stacking
Blending
Bagging
Boosting
In bagging and Boosting, there are other popular models like Gradient Boosting, Random Forest, XGBoost, etc.
We will be covering all these techniques comprehensively and with Python code in this course.
Do we use Ensemble Learning techniques only for classification or regression or both?
We can use ensemble learning for both types of machine learning problems: Classification and Regression. While techniques like Max Voting are used for classification, techniques like Random Forest, Gradient Boosting, etc can be used for both Classification and Regression problems.
What are the more popular ensemble learning techniques?
Ensemble learning techniques like Random Forest, Gradient Boosting and variants, XGBoost, and LightGBM are extremely popular in hackathons. Depending on the data we are dealing with, we can use these techniques as our machine learning models. For example, you can use LightGBM(Light Gradient Boosting) for large datasets, or CatBoost for when your data has categorical variables.
What is the intuition behind Bagging?
The idea behind bagging is combining the results of multiple models (for instance, all decision trees) to get a generalized result. Bagging (or Bootstrap Aggregating) technique uses subsets (bags) to get a fair idea of the distribution (complete set).

What is the intuition behind Boosting?
Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model on subsets of the data. The succeeding models are dependent on the previous model. The boosting algorithm combines a number of weak learners to form a strong learner and boost your overall results.
What is the difference between Bagging and Boosting?
While both bagging and boosting involve creating subsets, bagging makes these subsets randomly, while boosting prioritizes misclassified subsets. Additionally, at the final step in bagging, the weighted average is used, while boosting uses majority weighted voting.
Does ensemble learning improve my machine learning model?
In a word, yes! And that too drastically! Ensemble learn can improve the results of your machine learning even exponentially at times. There are two major benefits of Ensemble models:
More accurate predictions(closer to the actual value)
Combining multiple simple models to make a strong model improves the stability of the overall machine learning model.
As you will learn in the course, we will take a real-life dataset and study how ensemble learning improves the score of our machine learning model as compared to using only simple models.
Would knowing about ensemble learning help me crack interviews and hackathons?
Ensemble learning is the go-to method to achieve a high rank on hackathon leaderboards. You can go over the winning approaches of multiple hackathons, and there is a guarantee that a majority would have used an ensemble technique as their machine learning model.
Not only in hackathons but ensemble models are also used extremely popular in the industry because of how cost-effective they are. That is why, questions on random forest, gradient boosting, stacking, etc are often asked in interviews. Proposing an ensemble learning solution for a problem statement in an interview would always give you an edge over other solutions!
Who is the Ensemble Learning and Ensemble Learning Techniques Course for?
This course is designed for anyone who:
Wants to learn about Ensemble Learning in Machine Learning
Wants to expand their current machine learning skillset
Is a newcomer to Machine Learning
Is looking to ace machine learning hackathons
Is passionate about machine learning!
Instructor(s)
Enroll now
Course curriculum
1
Introduction
Intuition behind Ensemble Learning
What is Ensemble Learning?
What models will be covered in the course?
Quiz: Introduction to Ensemble Learning
AI&ML Blackbelt Plus Program (Sponsored)
2
Basic Ensemble Learning Techniques
3
Advanced Ensemble Learning Techniques
4
Advanced Ensemble Learning: Bagging
5
Advanced Ensemble Learning: Boosting
6
What next?
FAQ
Common questions related to the Convolutional Neural Networks (CNN) from Scratch course
Who should take the Ensemble Learning and Ensemble Learning Techniques course?
This course is designed for anyone who wants to understand how Ensemble Learning and the various Ensemble Learning techniques work. This is an important concept in machine learning that you would need to have a good grasp on.
I have decent programming experience but no background in machine learning. Is this course right for me?
You should ideally have a basic grasp on machine learning algorithms like decision trees and random forest. We suggest enrolling in our Getting Started with Decision Trees free course for starters.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Ensemble Learning and Ensemble Learning Techniques” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Ensemble Learning and Ensemble Learning Techniques” course in a few hours.
I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Appled Machine Learning” course.
Can I download the videos in this course?
Enroll in Ensemble Learning and Ensemble Learning Techniques
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Course Name : Linear Programming for Data Science Professionals || Course Description : Linear Programming for Data Science Professionals
The Linear Programming for Data Science Professionals’ course will guide you on how to get started with linear programming (LP), the different components of LP, and how to solve linear programming problems using Excel, R and more!
Enroll for free
Get a Head Start on Linear Programming to Solve Optimization Problems in Data Science!
Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company – everything uses optimization.
And that’s where learning linear programming will make you a better data science professional.
We are solving optimization problems everyday - without realizing it. Think of how you distributed the chocolate among your peers or siblings - that’s your way of optimizing the situation. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources.
And linear programming helps us solve these optimization problems with ease and efficiency. As a data science professional, you are bound to come across these optimization problems that you will solve using linear programming.
Simply put, you should know what linear programming is, and the different methods to solve linear programming problems.
Linear Programming Questions you should be able to answer:
What is linear programming?
Why should you learn linear programming?
What are the different linear programming terminologies?
What programming languages should you know to apply linear programming?
Can I solve a linear programming problem using Microsoft Excel?
What kind of projects can you do using linear programming?
Interview questions - what to expect around linear programming?
What are the applications of linear programming in real-life?

This course explains the concept of linear programming in simple English. We have kept the content as simple as possible so even beginners will be able to quickly pick up how linear programming works. You will, of course, also learn how to solve linear programming problems!

Who is the Introduction to Linear Programming for Data Science Professionals Course for?
This course is for anyone who:
Wants to learn about linear programming
Wants to understand how linear programming works and how to solve linear programming problems
Isn’t a programmer but is curious how to work on linear programming problems (MX Excel!)
Wants to master a niche branch of data science to gain a competitive advantage over their peers
Is looking to solve optimization problems using linear programming
What do you need to get started with the Introduction to Linear Programming for Data Science Professionals course?
Here’s what you’ll need:
A working laptop/desktop with 4 GB RAM
A working Internet connection
Working knowledge of Microsoft Excel
Optional - Basic knowledge of R
That’s it! You’re all set to learn linear programming and solve optimization problems!
Common Questions Beginners Ask About Linear Programming
What is linear programming?
As we discuss in the course: “Linear programming is a simple technique where we depict complex relationships through linear functions and then find the optimum points. The important word in the previous sentence is depict. The real relationships might be much more complex – but we can simplify them to linear relationships.”

Why should you learn linear programming?
I’m sure you’ve had this question ever since you came across linear programming. Well - here’s the good news. The use cases of linear programming are all around us.
We use linear programming in both our personal and professional fronts. We use linear programming when we are driving from home to work and want to take the shortest route. Or when we have a project delivery we make strategies to make our team work efficiently for on time delivery. You get the idea!

What are the different linear programming terminologies?
Here are the key terminologies you’ll come across when learning linear programming:
● Decision variables
● Objective function
● Constraints
● Non-negativity restriction
We will cover each of these terms in detail inside the course.

What programming languages should you know to apply linear programming?
We showcase how to solve a linear programming problem using R in the course. However, you don’t need to know programming to learn linear programming! As we show in the course, you can also use Excel and even manual methods to solve a typical linear programming problem.
In real life, you might need to rely a lot more on languages like R and Python since the dataset you’ll be working with might be too big for Excel.

Can I solve a linear programming problem using Microsoft Excel?
Of course! That’s another powerful aspect of linear programming - you don’t need to master a programming language to solve linear programming problems. The ‘OpenSolver’ feature of Excel works perfectly and is made for linear programming problems.

What kind of projects can you do using linear programming?
Any project that requires optimization can be used to apply linear programming. As you’ll see inside the course, we solve various types of linear programming problems, including a fascinating case study of a chocolate manufacturing business wanting to maximize its profits.

Interview questions - what to expect around linear programming?
This is an interesting question. Personally, this is a mixed round as far as data science interviews go. You might not get a direct question on linear programming, such as “How would you use linear programming to solve an optimization problem?”.
Instead, there’s a high probability of getting a case study like optimizing delivery routes. This is where your knowledge of linear programming will come in handy.
We have seen a lot of folks not paying attention to this branch of data science - and that’s a mistake. It’s an excellent tool to have at your fingertips and will make your life in the data science field a lot easier.

What are the applications of linear programming in real-life?
There are innumerable applications of linear programming in the real-world. Here are 4 key ones that you'll come across:
● Manufacturing industries use linear programming for analyzing their supply chain operations
● Organized retail for shelf space optimization
● Optimizing Delivery Routes. Think Zomato, Swiggy, Amazon, Uber, Ola, etc.
● Machine Learning algorithms!

Enroll for free
Introduction to Linear Programming for Data Science Professionals Course Curriculum
1
Introduction to Linear Programming
How to Use the Mini-Course Template
AI&ML Blackbelt Plus Program (Sponsored)
2
Introduction
3
Tools to Solving Linear Programming Problems
4
Methods to Solve Linear Programing Problems
5
Applications of Linear Programming
6
Conclusion
Instructor(s)
FAQ
Common questions related to the Introduction to Linear Programming for Data Science Professionals course
Who should take the Introduction to Linear Programming for Data Science Professionals course?
This course is designed for anyone who wants to understand what linear programming is, how it works, what are the different linear programming problems out there, and how to solve them. This is an under-appreciated topic in data science that will propel your skillset to a entirely different level.
I have decent programming experience but no background in machine learning. Is this course right for me?
Absolutely! This course covers a topic that is not reliant on machine learning knowledge. That’s the beauty of linear programming - you will rely on a bit of math and a programming language or tool (like MS Excel). That’s all you need to learn and solve linear programming problems!
What is the fee for the course?
This course is free of cost! All you need to do is sign up and get started.
How long would I have access to the “Introduction to Linear Programming for Data Science Professionals” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Introduction to Linear Programming for Data Science Professionals” course in a few hours.
I’ve completed this course and have a good grasp on linear programming. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.
Can I download the videos in this course?
We regularly update the “Introduction to Linear Programming for Data Science Professionals” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Enroll in Linear Programming for Data Science Professionals today
More than 1 Million users use Analytics Vidhya every month to learn Data Science. Start your journey now!
Get started now
Course Name : Naive Bayes from Scratch || Course Description : Naive Bayes from Scratch
Naive Bayes is a popular and widely used machine learning algorithm for classification problems. Learn what is Naive Bayes, how a Naive Bayes classifier works, and implement Naive Bayes yourself in this course!
Enroll for free
30 Mins
4.6/5
Advanced
Learning Naive Bayes from Scratch for Machine Learning
Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.
The beauty of Naive Bayes lies in it’s incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!
So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default - the Naive Bayes algorithm will get you on your way.
Who is the Naive Bayes from Scratch Course for?
This course is for anyone who:
Wants to learn the Naive Bayes algorithm from scratch
Wants to expand their skillset to learn a popular machine learning algorithm
Is curious about solving a classification problem using a different approach

The Naive Bayes algorithm is a classic that’s much loved by data scientists all around the world. It's easy to learn and will serve you well in your data science journey.

What do you need to get started with the Naive Bayes course?
A working laptop/desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Machine Learning
Basic knowledge of Python / R - check out this Course first, if you are new to Python
Course curriculum
1
Probability
Key Terms and Definitions
Introduction to Probability
Quiz: Introduction to probability
Calculating Probabilities of events
Quiz: Calculating Probabilities of events
AI&ML Blackbelt Plus Program (Sponsored)
2
The Naive Bayes Algorithm
3
What Next?
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Common Questions Beginners Ask About Naive Bayes
What is Naive Bayes?
In machine learning, the Naive Bayes is a classification algorithm based on the concept of Bayes Theorem. Bayes theorem is one of the fundamental theorems in probability. We will be learning all these concepts in the course quite thoroughly
How are Naive Bayes and probability related?
The Bayes Theorem forms the backbone of the Naive Bayes algorithm. We use conditional probability to classify the data - thus, the Naive Bayes algorithm basically gives us the probability of a record being in a particular class, given the values of the features. This is nothing but conditional probability!
What are the assumptions we need to keep in mind for using the Naive Bayes algorithm?
The most important assumption that Naive Bayes makes is that all the features are independent of each other. You might also need to convert the continuous variables into discrete variables.

What are the different types of Naive Bayes algorithms?
There are 3 main types of Naive Bayes algorithms:
Gaussian Naive Bayes
Multinomial Naive Bayes
Bernoulli Naive Bayes
We will be covering all these techniques comprehensively and with Python code in this course.
Can Naive Bayes be used only for binary classification?
Not at all - in fact, the Naive Bayes classifier is one of the most popular algorithms for multiclass classification. Depending on the data we are dealing with, we can use any of the 3 types of the Naive Bayes algorithms
How does Naive Bayes hold up against other classification algorithms?
Though classification algorithms like Logistic Regression, random forest, etc are quite popular, Naive Bayes holds its own among them. It is faster than Random forest, since it can adapt to changing data pretty quickly. If the assumptions of Naive Bayes hold true, then it is much faster than logistic regression as well.
What are the main advantages of Naive Bayes?
The main advantages are:
It is fast, and easy to understand
It is not prone to overfitting
It does not need much training data
What are the main advantages of Naive Bayes?
Like all other algorithms, Naive Bayes has its own set of cons as well:
It does not work that well when the number of features is very high
The assumption that input features are independent of each other may not always hold true
You might lose important information while discretising the continuous variables.
What are the applications of the Naive Bayes algorithm?
Though the algorithm is relatively simple to understand, you cannot underestimate the power of the Naive Bayes classifier! It is fast, intuitive, and is used for text classification tasks. Since it can be used for multiclass classification as well, it is considered a very versatile and flexible classifier. A majority of research papers on text classification start off by using the Naive Bayes classifier as the baseline model.
FAQs
Who should take the Naive Bayes course?
I have decent programming experience but no background in machine learning. Is this course right for me?
What is the fee for the course?
How long would I have access to the “Naive Bayes from Scratch” course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on Naive Bayes. What should I learn next?
Can I download the videos in this course?
Course Name : Learn Swift for Data Science || Course Description : Learn Swift for Data Science
Swift for Data Science introduces you to the wonderful world of the Swift programming language and how to use it for data science tasks. Swift is quickly being adopted by data science organizations so start your journey today.
Enroll for free
Your Guide to Learning Swift for Data Science from Scratch
he Swift programming language is quickly becoming the language of choice for a lot of data science experts and professionals. Swift’s flexibility, ease of use, excellent documentation, and quick execution speed are key reasons behind the language’s recent prominence in the data science space.
Swift is a more efficient, stable and secure programming language as compared to Python. In fact, Swift is also a good language to build for mobile. In fact, it’s the official language for developing iOS applications for the iPhone!
The cherry on the cake for Swift? It has the support of the likes of Google, Apple, and FastAI behind it!
“I always hope that when I start looking at a new language, there will be some mind-opening new ideas to find, and Swift definitely doesn’t disappoint. Swift tries to be expressive, flexible, concise, safe, easy to use, and fast. Most languages compromise significantly in at least one of these areas.” – Jeremy Howard
And when Jeremy Howard endorses a language and starts using it for his daily data science work, you need to drop everything and listen.
In this free course on Swift for Data Science, we will learn about Swift as a programming language and how it fits into the data science space. If you’re a Python user, you’ll notice the subtle differences and the incredible similarities between the two. We showcase Swift code as well in the course so get started!

In this free course on Swift for Data Science, we will learn about Swift as a programming language and how it fits into the data science space. If you’re a Python user, you’ll notice the subtle differences and the incredible similarities between the two. We showcase Swift code as well in the course so get started!
Get started now
Why Swift for Data Science?
It’s a fair question. Most data science folks you’ll talk to will encourage you to learn Python before Swift. But as we covered above, Swift has its own share of advantages over Python as the programming language you should learn for data science.
Here’s Jeremy Howard espousing the value and awesomeness of the Swift programming language:
Key Questions To Answer for Swift Programming Beginners:
What is Swift?
Are Swift and Python similar?
If not, then how is Swift programming different from Python?
Why is Swift required to perform data science tasks?
What are some industrial use cases of Swift for Data Science?
Can we use Swift for deep learning as well?
What are some unique features of Swift?
What are some key challenges of Swift for Data Science?
How is Swift being used in the industry?
Who is the Swift for Data Science Course for?
The Swift for Data Science course is targeted towards anyone who:
Wants to learn data science using a new but upcoming programming language
Wants to learn the Swift programming language
Is interested in exploring the world of data science
Is curious to explore a new programming language beyond Python!
What do you need to get started with the Swift for Data Science course?
Here’s what you’ll need:
A working laptop/desktop with 4 GB RAM
A working Internet connection
Basic knowledge of core machine learning algorithms
Optional - Knowledge of Python programming will help you appreciate the differences between that and Swift plus help you make the transition easier
You’re ready to take your first steps into the world of Swift programming for Data Science!

Get started now
Common Questions Beginners Ask About Swift
We saw these questions earlier. Let’s quickly go through them one by one.
What is Swift?
Swift is an open-source programming language announced in 2014. Though it is a general-purpose language, it is fast gaining ground in the Data Science space.
Are Swift and Python similar?
Though Swift and Python are different languages, they are similar in many ways. Both are open-source, though the Swift stack also has a different version for macOS users. Both swift and Python include some functional programming tools and a lot of the data structures in both Python and Swift are similar - like lists, tuples, dictionaries, etc.
How is Swift different from Python?
The syntax of Swift is slightly different from that of Python. Just like Python, Swift was essentially developed by Apple as a holistic mobile application programming language, but its uses in Data Science and Machine Learning catapulted it to fame. The other differences are:
Swift is much faster than Python
Swift catches your error before runtime, unlike Python - where you catch your mistakes after the code has run
Python is essentially a wrapper on C, unlike Swift which is its own standalone language
Why do I need to learn about Swift?
In the fast-evolving field of Data Science, it is important to stay updated on recent developments. Just like Python replaced R, there has been a slew of new programming languages geared towards speed and performance - Swift leads the pack here. Though much newer, it is now in the top 10 languages used by developers and the industry’s support for it only enhances the need for learning Swift.
How can I use Swift for Data Science Purposes?
You need no better endorsement of technology than Google. One of the most popular machine learning frameworks, Tensorflow introduced a Swift for TensorFlow in 2018. This now combined with other Open Source libraries like SwiftAI, SwiftPlot, etc builds a powerful arsenal of tools for Data Science. Not only this, but for MacUsers, it is compatible with the CoreML library - you can now use it in Google Colab
Can Swift be used for Deep Learning too?
In short, yes! Swift for Tensorflow(S4TF) library supports deep learning architectures in Swift. Along with CoreML, Swift also has pre-trained models for ComputerVision like ImageNet and ResNet, and GPT-2, BERT, etc for NLP
What are some other unique features of Swift?
What if you could develop a mobile application which automatically tells you if it is a dog or a cat that you are looking at? Since Swift can be combined with CoreML, it can also be used for data science functionalities in mobile applications. Some other unique features are:
It is compatible with Python and C.This means that Python libraries like Numpy, Scikit-learn etc. can be easily used just by importing them in Swift
Other Python APIs can also be used with just importing Python
Apart from macOS, Swift can also be used in watchOS, tvOS, etc. So it can easily be the next big thing in IoT
What are the main disadvantages of Swift?
There are some disadvantages of Swift as well.
The language is very new compared to a veteran language like Python - so it is still under continuous development
Since newer versions of Swift keep coming up, it tends to make development in Swift unstable

How is Swift being used in the industry?
Swift is being used by top companies both as a general-purpose programming language and for Data Science as well. Companies using Swift include Uber, Slack, Lyft, LinkedIn, etc. Stay ahead of the curve before the migration from Python happens by enrolling in this free tutorial!
Get started now
Course curriculum
1
Introduction
Getting Started
Why Swift?
AI&ML Blackbelt Plus Program (Sponsored)
2
Swift Basics for Data Analysis
3
Machine Learning with Swift and TensorFlow
4
Bonus Chapter: NLP Based iOS Apps uwing Swift
5
What Next?
Instructor(s)
FAQ
Common questions related to the Swift for Data Science course
Who should take the Swift for Data Science course?
We have curated the Swift for Data Science course for beginners with the Swift programming language. You should take the course if you are interested in broadening your data science skillset, learning a new programming language, and want to add a hot and upcoming tool to your budding data science resume!
I have decent programming experience but no background in machine learning. Is this course right for me?
You would need to have an idea of the core machine learning algorithms. We will guide you on how to use Swift for data science to build machine learning algorithms so you would at least need to know what is supervised and unsupervised learning, and the difference between regression and classification problems.
What is the fee for the course?
This course is free of cost!
How long would I have access to the “Swift for Data Science” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enrol in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Swift for Data Science” course in a few hours.
I’ve completed this course and have a good grasp on the Swift programming language. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.
Can I download the videos in this course?
We regularly update the “Swift for Data Science” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Enroll in Learn Swift for Data Science
More than 1 Million users use Analytics Vidhya every month to learn Data Science. Start your journey now!
Get started now
Course Name : Introduction to Web Scraping using Python || Course Description : Introduction to Web Scraping using Python
What is web scraping? Why is web scraping a must-know skill? How can you perform web scraping in Python? This course will cover all these aspects of web scraping and showcase how to perform web scraping using BeautifulSoup and Scrapy.
Enroll for free
Become Familiar with Web Scraping using Python
The need and importance of extracting data from the web is becoming increasingly loud and clear. There is an unprecedented volume of data on the internet right now - and data science projects often need this data to build predictive models.
That’s a key reason why data scientists are expected to be familiar with web scraping.
We have found web scraping to be a very helpful technique for gathering data from multiple websites. Some websites these days also provide APIs for many different types of data you might want to use, such as Tweets or LinkedIn posts.
But there might be occasions when you need to collect data from a website that does not provide a specific API. This is where having the ability to perform web scraping comes in handy. As a data scientist, you can code a simple Python script and extract the data you’re looking for.
So knowing how to perform web scraping using Python will help you go a long way towards becoming a resourceful data scientist. Are you ready to take the next step and dive in?
A note of caution here – web scraping is subject to a lot of guidelines and rules. Not every website allows the user to scrape content so there are certain legal restrictions at play. Always ensure you read the website’s terms and conditions on web scraping before you attempt to do it.
In this course, we will dive into the basics of web scraping using Python. We will understand what web scraping is, the different Python libraries for performing web scraping, and finally we’ll implement web scraping using Python in a real-world project. There’s a lot to unpack here so enroll today and start learning!
Questions Beginners have about Web Scraping using Python
We’re sure you’ve asked these questions before. Even if you haven’t, you should start learning how these web scraping questions should be answered:
What is web scraping?
Why should you learn web scraping?
Why Python for web scraping?
What are the different Python libraries for performing web scraping?
Can I use R for web scraping?
What kind of projects can I take up after learning web scraping?
Are web scraping concepts asked in data science/machine learning interviews?
You’ll learn about these concepts inside the course and we have even provided a high-level overview of these questions after the course curriculum below.

Who is the Introduction to Web Scraping using Python Course for?
This course is for anyone who:
Wants to learn the art of web scraping using Python
Is looking to collect or gather more data for their data science or machine learning project
Wants to add a new and crucial skill to their existing data science portfolio
Is curious about Python programming
What do you need to get started with the Introduction to Web Scraping using Python course?
Here’s what you’ll need:
A working laptop/desktop with 4 GB RAM
A working Internet connection
Basic knowledge of Python. You can take this free Python course if you need a refresher
That’s it! You’re all set to perform web scraping on your machine!
Call to action
This is where you seal the deal. Sprinkle this section throughout your page to push prospects to purchase!
Get started now
Course curriculum
1
Introduction to Web Scraping
What is Web Scraping?
Caution
Popular Libraries for Web Scraping
Components of Web Scraping
AI&ML Blackbelt Plus Program (Sponsored)
2
Web Scraping: Procedure
3
Scraping URLs and Email IDs from a Web Page
4
Scrape Images in Python
5
Scrape Data on Page Load
Instructor(s)
Common Questions Beginners Ask about Web Scraping
Here, we break down the common questions beginners often have on web scraping
What is web scraping?
Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).
You can perform web scraping in various ways, including use of Google Docs to almost every programming language.
Why should you learn web scraping?
Web scraping is incredibly useful when you don’t have enough data with you to train a machine learning model. Web scraping helps us to collect this data from websites (if permitted) and we can then use that to train our model. You can imagine why web scraping is such a prized tool in a data scientist’s arsenal!
Why Python for web scraping?
Python is the most popular tool out there in the world for Web Scraping. Its 2 prominent libraries - BeautifulSoup and Scrapy makes web scraping easy and efficient. Python’s syntax makes understanding of the codes easy. Also python provides many other libraries for web scraping which can be used as per our needs. Eg- lxml, requests etc
What are the different Python libraries for performing web scraping?
There are many libraries in Python that help us to scrape the web. The 3 most prominent libraries include:
BeautifulSoup
Scrapy
Selenium
Can I use R for web scraping?
You sure can! You can perform web scraping in both Python and R. We are teaching you how to do this using Python in the course but feel free to use R if that’s your language of choice. You can go through this tutorial that walks you through how to master web scraping using an R package called rvest.
Are web scraping concepts asked in data science/machine learning interviews?
This depends a lot on the data science role and the organization you’re interviewing for. Not all organizations require you to know or apply web scraping. But here’s why you should learn it anyway - it will help you expand your skillset and also help you work on your personal projects for data science. There’s a lot to learn and nothing to lose!
FAQ
Who should take the Introduction to Web Scraping using Python course?
This course is designed for anyone who wants to learn everything about getting started with web scraping using Python. Web scraping is an incredibly useful tool to have in your data scientist’s armoury and this course will get you started on the right footing.
I have decent programming experience but no background in machine learning. Is this course right for me?
Absolutely! This course covers a topic that is not reliant on machine learning knowledge. All you need are basic Python programming skills - everything else will fall into place as you go through the contents of the course.
What is the fee for the course?
This course is free of cost! All you need to do is sign up and get started.
How long would I have access to the “Introduction to Web Scraping using Python” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
How much effort do I need to put in for this course?
You can complete the “Introduction to Web Scraping using Python” course in a few hours.
I’ve completed this course and have a good grasp on linear programming. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.
Can I download the videos in this course?
We regularly update the “Introduction to Web Scraping using Python” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
Enroll in Introduction to Web Scraping using Python today
More than 1 Million users use Analytics Vidhya every month to learn Data Science. Start your journey now!
Get started now
Course Name : Tableau for Beginners || Course Description : Tableau for Beginners
Tableau is the tool of choice for business intelligence, analytics and data visualization experts. Learn how to use Tableau, the different features of Tableau, and start building impactful visualization using this Tableau tutorial!
Enroll for free
15 Mins
4.6/5
Beginner
Get Started with Tableau for Data Visualization, Analytics and Business Intelligence
Tableau is the gold standard in business intelligence, analytics and data visualization tools. Tableau Desktop (and now Tableau Public) have transformed the way we interact with visualizations and tell data stories to our clients, stakeholders, and to non-technical audiences around the world.
Tableau has been recognized as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 8 straight years. Here’s Gartner’s most recent ranking in 2020:

In this Tableau for Beginners course, you will learn everything you need to get started with this wonderful visualization and business intelligence tool. You’ll be able to build charts like bar charts, line charts (for working with time series data), pie charts, and even get the hang of geospatial analysis using map visualizations in Tableau!
Note: If you’re looking to build and master dashboards and storyboards in Tableau, make sure you check out the popular ‘Mastering Tableau from Scratch: Become a Data Visualization Rockstar’ course!
Course curriculum
1
Introduction
Welcome to the Course
AI&ML Blackbelt Plus Program (Sponsored)
2
Concept of Visualization
3
Understanding the Length and Breadth of Tableau
4
Getting Started with Tableau
5
Different Types of Charts in Tableau
6
BONUS: Other Functionalities in Tableau
7
What's Next?
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!.
Common Questions Beginners Ask about Tableau
Why should you use Tableau?
Tableau, as we mentioned above, is the gold standard in analytics and business intelligence. It is a widely used tool in the industry, both in big firms as well as startups. Tableau helps us create effective, impactful and beautiful dashboards and stories that our clients and stakeholders love.
There are a ton of job vacancies for Tableau professionals in the industry so this is a great time to get started.
Tableau Desktop or Tableau Public - which one should I choose?
Tableau Desktop and Tableau Public are two of the many offerings by Tableau. Tableau Desktop is the premium version of Tableau Public. It is used by organizations worldwide for their analytics and business intelligence projects. You will be working with Tableau Desktop in the industry.
Tableau Public, on the other hand, is a free platform to learn Tableau. It was created with the purpose of making the broader audience comfortable with Tableau and how it works. Tableau Public offers almost all the features of Tableau Desktop (except you can’t save your work on your local machine - it will be uploaded to Tableau’s public gallery).
You can perform all the work provided in this course in Tableau Public.
What are the different tools under the Tableau umbrella?
Tableau offers a variety of tools catering to different organizations and solutions:
Tableau Desktop
Tableau Public
Tableau Prep
Tableau Server
Tableau Online

And many other tools you can check out on Tableau’s official website.
Where can you learn about the intermediate-level of Tableau?
Excellent question! We recommend taking the ‘Mastering Tableau from Scratch: Become a Data Visualization Rockstar’ course to deep dive into Tableau. You will learn how to quickly convert your data into actionable insights, create dashboards to impress your clients, and learn Tableau tips, tricks and best practices in your day-to-day role.
Can I use Tableau for time series forecasting?
Yes, you can. Tableau has an in-built feature for forecasting where you can use the concept of moving average to build forecasts. But keep in mind that this is a crude feature and might not be reliable for your business. Time series is a complex topic and has advanced beyond moving average.
However, Tabelau’s forecast feature does give you a rough idea of what to expect and might work for small businesses or if you’re looking for a quick idea of what to expect from your sales numbers, for example.
Do you need to know programming to learn Tableau?
Not at all. Tableau thrives as a drag-and-drop tool (for the most part). That’s the beauty of Tableau to be honest, you can quickly get started and build awesome visualizations without having to get into any coding or programming.
But knowing simple Excel formulas, such as IF-ELSE, will help you with feature engineering in Tableau. And that is a critical part of a business intelligence analysts skillset.
What kind of projects can you perform using Tableau?
You can take up all sorts of analytics projects next. We suggest heading over to the DataHack platform and picking up any project that catches your interest. Load the dataset into Tableau and get going!
FAQs
Who should take the Introduction to Tableau for Beginners course?
I have worked with visualizations but have no background in analytics or business intelligence. Is this course right for me?
What is the fee for the course?
How long would I have access to the “Tableau for Beginners” course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on the basics of Tableau. What should I learn next?
Can I download the videos in this course?
Course Name : Getting Started with Neural Networks || Course Description : Getting Started with Neural Networks
Kick start your journey in deep learning with Analytics Vidhya's Introduction to Neural Networks course! Learn how a neural network works and its different applications in the field of Computer Vision, Natural Language Processing and more.
Enroll for free
70 Hours
4.7/5
Beginner
Introduction to Neural Networks
What is a neural network? How does it work? What does a neural network do? Learn neural networks for free in this course and get your neural network questions answered, including applications of neural networks in deep learning.

Learn how neural networks work in deep learning
Do you want to acquire a super power? How about learning neural networks? Neural networks are at the heart of the deep learning revolution that’s happening around us right now.
Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.
But as a beginner in this field, you’ll have a ton of questions:
What is a neural network?
Why do we need to learn neural networks?
How popular are neural networks?
What are the advantages of neural networks?
What kind of challenges you could face when applying neural networks?
What exactly should you learn about neural networks?
What are the core concepts that make up neural networks?
What are the different types of neural networks in deep learning?
Do you need to know programming to build a neural network?
Which programming language is best for building neural networks? Python or R?
What are the different applications of neural networks?
What kind of problems or projects can you solve using neural networks?

From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us. Thanks to the idea of neural networks like CNN and RNN, deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis!
So how can you get started with Neural networks?
Where should you begin learning? Neural networks can appear to be complex to master. In a lot of ways, they are. We have seen quite a number of aspiring data scientists and deep learning enthusiasts give up before they even touched a neural network!
But they’ve gone about it the wrong way. There are a lot of myths about neural networks that force people to quit:
You need a strong background in statistics, machine learning, linear algebra, and calculus to learn neural networks
You need to have a Ph.D. in order to understand neural networks
You cannot build a neural network without advanced mathematics knowledge
Mastering all of these would take you years!
So, how else can you learn neural networks?
That is EXACTLY what this free course by Analytics Vidhya will teach you.
Course curriculum
1
Introduction to Deep Learning
What is Deep Learning?
Difference b/w Deep Learning and Machine Learning
Why Deep Learning is so popular?
AI&ML Blackbelt Plus Program (Sponsored)
2
Getting ready for the course
3
Introduction to Neural Network
4
Activation Functions
5
Loss Function
6
NN on structured Data
7
Assignment: Big Mart Sales Prediction
8
Real World Use cases of Deep Learning
9
Where to go from here?
Enroll now
Certificate of Completion
Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Common Questions Deep Learning Beginners ask about Neural Networks
Why do we need to learn neural networks?

Why are neural networks so popular?

What are the advantages of neural networks?

What kind of challenges could you face when applying neural networks?

What exactly should you learn about neural networks?

What are the core concepts that make up a neural network?

What are the different types of neural networks in deep learning?

Which programming language should I learn to build neural networks?

What kind of problems or projects can I solve using neural networks?
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
FAQs
Who should take this course?
I have a programming experience of 2+ years, but no background in Deep Learning. Is the course right for me?
What is the fee for this course?
How long would I have access to the “Introduction to Neural Networks” course?
How much effort will this course take?
How can I apply and test my learnings about Neural Networks?
I’ve completed this course already and have a decent knowledge about neural networks. What should I learn next?
. Can I download videos from this course?
Which programming language is used to teach Neural Networks in this course?
Course Name : Introduction to AI & ML || Course Description : Introduction to AI & ML
Artificial Intelligence (AI) and Machine Learning (ML) are changing the world around us. From functions to industries, AI and ML are disrupting how we work and how we function. Get to know all about the different facets of AI and ML in this course.
Enroll for free
2 Hours
4.8/5
Beginner
Welcome to the World of Artificial Intelligence and Machine Learning!
The AI revolution is here - are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?
Analytics Vidhya’s ‘Introduction to AI and ML’ course, curated and delivered by experienced instructors with decades of industry experience between them, will help you understand the answers to these pressing questions.
Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function - from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.
And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!
What do I need to start with Introduction to AI & ML course?
A working Internet connection
Curiosity about Artificial Intelligence and Machine Learning

This is all it takes for you to start your journey in Artificial Intelligence and Machine Learning
What are you waiting for?
Course curriculum
1
Introduction to AI & ML
What is AI&ML?
Types of ML
When to Apply AI&ML
Recent AI Uprising
How the world is Changing?
Building Blocks of AI and ML
Knowing Each Other
AI&ML Blackbelt Plus Program (Sponsored)
2
Common Terminologies, Tools and Techniques
3
Skills required to become a data science professional
Enroll for free
Certificate of Completion
Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Key Takeaways of the Introduction to AI & ML Course
You will learn the current state of AI and ML, how they are disrupting businesses globally
Solid understanding of what AI and ML mean, what they represent in the current market and industry, how they work, and why you should learn about them.
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
FAQs
I have decent programming experience but no background in AI and ML. Is this course right for me?
What is the fee for the course?
How much effort do I need to put in for this course?
I’ve completed this course and have a good grasp on what AI and ML mean. What should I learn next?
Can I download the videos in this course?
Course Name : Winning Data Science Hackathons - Learn from Elite Data Scientists || Course Description : Winning Data Science Hackathons - Learn from Elite Data Scientists
Competing in a data science hackathon is all about skills, tactics, creativity and learning! Here is a unique opportunity to understand how the top hackers approach various types of problem statements and competitions.
Enroll for free
About the course
There is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.
This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks.
This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R and many more!
From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!
Prerequisites for the Winning Data Science Hackathons course
A working knowledge of basic machine learning algorithms will help you understand the talks covered in this course.
Course curriculum
1
Introduction to Winning Data Science Hackathon Course
About the Winning Data Science Hackathon course
AI&ML Blackbelt Plus Program (Sponsored)
2
Talks by Elite Data Scientists
What you’ll learn in the Winning Data Science Hackathons Course
Here’s a summary of each expert talk in the course.
Automating the Machine Learning Pipeline with AutoML by Dr. Sunil Chinnamgari
AutoML and the suite of tools available in this area attempts to automate all of these tasks of a data scientist therefore enabling almost a one – click ML pipeline development. This talk attempts to introduce the concept of autoML to the participants.
What Sets the Top Hackers Apart? - A panel discussion of elite data science practitioners
A panel discussion consisting of top data scientists - Sourabh Jha, Kiran R, Mohsin Hasan Sudalai Rajkumar (SRK), Sahil Verma, Rohan Rao. They will discuss many elements of data science competitions but most importantly - What does it take to repeatedly perform well in these data science challenges?
Effective Feature Engineering for Building Better ML Models by Dipanjan Sarkar
Dipanjan takes a structured and comprehensive hands-on approach to feature engineering, where we will explore two interesting case studies based on real-world problems!
Feature Engineering for Image Data by Pulkit Sharma and Aishwarya Singh
Not all of us have unlimited resources like the big technology behemoths such as Google and Facebook. So how can we work with image data if not through the lens of deep learning? Here’s an amazing talk to up your image data skillset.
FAQ
Do I need to install any software as part of the course?
No, you don’t need any software for this course. A working internet connection is enough to get you started!
Do I need to take the modules in a specific order?
No, you can pick and choose the talks you want to listen to in any order you prefer.
How long can I access the course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
Where can I watch all the videos from DataHack Summit?
You can checkout the full DataHack Summit 2019 talks here:
https://courses.analyticsvidhya.com/courses/datahack-summit-2019
Course Name : Hypothesis Testing for Data Science and Analytics || Course Description : Hypothesis Testing for Data Science and Analytics
Hypothesis testing is one of the most fascinating steps data scientists perform and the most essential one! What is a hypothesis? How do I validate my hypothesis? Learn all the basics of hypothesis testing and how to implement them in your project!
Enroll for free
Statistics is the study of the collection, analysis, interpretation, presentation, and organisation of data. For all the data science and machine learning enthusiasts it is paramount to be well versed with various statistical concepts such as Hypothesis testing
Every day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.
These ideas that we come up with on such a regular basis – that’s essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is called hypothesis testing.
Statistics is the Grammar of Data Science
The course is structured in a manner that you will get ample of examples in each module. You’ll get to learn all about
Fundamentals of Hypothesis Testing: The course begins with a simple-to-understand example on hypothesis testing. This chapter will clear all your basics like - Null Hypothesis, Alternative Hypothesis, Type 1 Error, Type 2 Error, and Significance Level.
p-value
What is the Z Test? The course introduces the most basic type of testing a hypothesis - Z test. Z tests are a statistical way of testing. The chapter covers the one sample as well as the two sample Z test.
What is the t-Test? The t-test is another test for validating the hypothesis. The chapter begins with a unique example and later covers both the one-sample and two-sample t-test.
Deciding between the Z Test and t-Test Are you confused on how to use these tests? Don’t worry, the course comprises a simple step-by-step guide on choosing the best test for your experiment.
Case Study: Hypothesis Testing for Coronavirus in Python You’ll get to put your theoretical knowledge into practice and see how well you can do. You will get to work on a hypothesis testing case study on the corona virus dataset in Python!
Prerequisites for the Hypothesis Testing for Data Science and analytics
A basic knowledge of descriptive statistics like - mean, median, mode, variance and standard deviation. Experience in Python is a plus!
Course curriculum
1
Introduction to the course
Introduction to Hypothesis Testing Course
AI&ML Blackbelt Plus Program (Sponsored)
2
Fundamentals of Hypothesis Testing
3
What is the Z Test?
4
What is the t-Test?
5
Deciding between Z Test and T-Test
6
Case Study: Hypothesis Testing for Coronavirus using Python
FAQ
You’ll get to put your theoretical knowledge into practice and see how well you can do. You will get to work on a hypothesis testing case study on the corona virus dataset in Python!
A working knowledge of basic statistics would be helpful. Even though we have designed this course for beginners, knowing a bit about basic statistics will help you visualize certain concepts in a more vivid manner.
What is the fee for the course?
This course is free of cost.
How much effort do I need to put in for this course?
You’ll be able to finish this course in a week’s time if you spend an hour on it daily and explore it on your own along with what you learn here.
I’ve completed this course and have a good grasp on Hypothesis Testing. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. There are plenty of options to choose from. We suggest heading to courses.analyticsvidhya.com and browsing through the various offerings available.
You should also incorporate hypothesis testing in your daily life! Try and use problem statements from around you and build hypotheses based on those/ That’s the best way to learn.
Do I need to install any software as part of the course?
You will not require any software for the learning modules. Python and other libraries are necessary to solve the case study.
How long can I access the course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
Can I download the videos in this course?
We do not allow videos to be downloaded from the platform. You can visit this free course anytime to refer to these videos.