title,content Frameworks for Effective Problem Solving,"Frameworks for Effective Problem Solving Master the art of structured thinking to tackle complex challenges effectively. This course covers essential frameworks like SMART, MECE, and Issue Trees, guiding you in defining and solving problem statements while avoiding common pitfalls. Learn practical, step-by-step methods to enhance decision-making and problem-solving skills. 1 Structured Thinking Course Introduction Reading: The Global Problem Statement Stages of a Project and Frameworks Structured Thinking and Problem Solving Reading: Solving Problems Like a Pro Quiz 2 Frameworks of Problem Solving 3 Defining Problem Statements" Anyone can Build AI Agents - Free Course,"Anyone can Build AI Agents - Free Course Unlock the power of AI without any programming! This beginner-friendly course is designed for individuals who want to harness Generative AI’s potential without diving into code. In this course, you’ll learn to create, deploy, and customize an AI agent using an intuitive no-code platform Wordware. Step by step, we’ll guide you through setting up your agent to handle real-world tasks. Throughout the course, you’ll explore various tools, leverage pre-built AI models, and customize your agent’s behavior without writing a single line of code. By the end, you will be able to build your own AI agent ready to tackle business and personal projects, with the skills to innovate faster and more efficiently. 1 A for Agents A for Agents Agents In, Coding Out! Exploring Wordware Build your first Agent Quiz" A Comprehensive Learning Path to Become a Data Analyst in 2025,"A Comprehensive Learning Path to Become a Data Analyst in 2025 Where do I begin? Data Analyst is such a huge field - where do you even start learning about Data Analyst? These are career-defining questions often asked by data analyst 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 analytics! Here’s the learning path for people who want to become a data analyst in 2025. 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 analyst. Moreover, we have added the most in-demand skills for the year 2025 for data analyst along with exercises and assignments. 1 Overview of the Learning Path 2025 Overview of Learning Path Action Plan AI&ML Blackbelt Plus Program (Sponsored) 2 Step 1: Data Analyst Toolkit 3 Step 2: Data Visualization 4 Step 3: Data Exploration 5 Step 4: Advanced Data Anlysis and Foundation of Machine Learning" Reimagining GenAI: Common Mistakes and Best Practices for Success,"Reimagining GenAI: Common Mistakes and Best Practices for Success Generative AI is transforming industries, but implementation challenges often hinder its success. This course, led by experienced GenAI expert Shabazz Mohammed, dives deep into the pitfalls of adopting generative AI and provides actionable best practices to overcome them. Learn from real-world scenarios, understand why hi-fi demos fail in production, and gain insights into scalable, ethical, and ROI-driven AI adoption. 1 Common Mistake and Best practices Introduction State of GenAI in 2024 Common Mistakes Best Practices for Success Demo- GenAI Chatbot Alignment Case Study- Top US Bank Co-pilot on CLO Documents" Coding a ChatGPT-style Language Model from Scratch in PyTorch,"Coding a ChatGPT-style Language Model from Scratch in PyTorch Master the art of building a ChatGPT-style language model from scratch with PyTorch. In this course, you'll explore essential NLP concepts, implement transformers, and create a decoder-only architecture step-by-step. Guided by expert tutorials, gain practical skills to develop advanced AI models tailored for real-world applications. 1 Coding a ChatGPT Introduction Importing Creating Inputs & Lables Position Encoding Masked Self-Attention Putting Pieces Together Creating Decoder Only Transformer" Mastering Multilingual GenAI Open-Weights for Indic Languages,"Mastering Multilingual GenAI Open-Weights for Indic Languages Mastering Multilingual GenAI – Open-Weights for Indic Languages"" is a course designed to equip you with the knowledge to develop state-of-the-art multilingual AI models using open-weight architectures. Focusing on low-resource languages, particularly Indic languages, the course covers essential topics like multilingual AI training, instruction fine-tuning, model building, and performance evaluation. 1 Mastering Multilingual GenAI Introduction Importance of Multilingual Training for Multilingual Gen AI Instruction Fine-Tuning Data for Multilingual Measuring Performance for Multilingual Building a Model Human Preferences Curse of Multilinguality Coding Hands-On" Learning Autonomous Driving Behaviors with LLMs & RL,"Learning Autonomous Driving Behaviors with LLMs & RL This course dives into the development of autonomous driving behaviors using Reinforcement Learning (RL) and Large Language Models (LLMs). You’ll explore how RL agents are trained to navigate complex, real-world environments while making safe, human-like driving decisions. The course tackles key challenges such as designing effective reward systems, ensuring safety in high-speed driving scenarios, and improving the interpretability of AI decisions. Through practical projects, you will design RL agents using techniques like Deep Q-Networks (DQN), experience replay, and integrate LLMs to enhance decision-making. 1 Learning Autonomous Driving Behaviors with LLMs & RL Introduction Evolution RL Understanding RL Challenges with RL Approach to the Problem Statement Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL" GenAI Applied to Quantitative Finance: For Control Implementation,"GenAI Applied to Quantitative Finance: For Control Implementation This course explores the application of Generative AI in quantitative finance, focusing on building sustainable trading algorithms through keyword extraction, sentiment analysis, and time-series forecasting. Learn to predict commodity prices, such as gold, by integrating data from financial news sources, leveraging sentiment analysis, and optimizing models for robust trading signals. This course is ideal for finance professionals, quantitative analysts, data scientists, and traders looking to leverage AI for advanced financial modeling and decision-making. It's also perfect for technology enthusiasts and students eager to explore the intersection of AI and finance, enhancing their skills in algorithmic trading, risk management, and predictive analytics. What real-world applications are covered? You’ll explore AI in trading, risk management, and portfolio optimization, with practical examples of AI-driven finance solutions. Is there hands-on experience with AI tools? Yes, you'll work with Python-based AI tools and machine learning techniques directly applied to finance problems. How does the course address control implementation? The course teaches how to implement and maintain reliable AI models, focusing on risk mitigation and performance optimization. What projects or assessments are involved? You'll design trading algorithms, test risk models, and apply GenAI techniques to real-world financial challenges. How will this course benefit my career? You’ll gain cutting-edge AI skills in finance, positioning you for roles like quant analyst, data scientist, or AI strategist. 1 Gen AI Applied to Quantitative Finance Introduction Overview Problem Definition: Commodity Price Prediction Architecture Hands-On" "Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy","Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy This course provides a concise guide to optimizing Large Language Models (LLMs) by navigating tradeoffs in speed, cost, scale, and accuracy. Learn practical techniques like LoRA, model quantization, and parameter-efficient fine-tuning to improve performance while reducing costs. You'll explore various deployment strategies and understand how to evaluate LLMs using industry-standard benchmarks, making this course ideal for anyone seeking efficient, scalable AI solutions. 1 Navigating LLM Tradeoffs Introduction Resources Technique to Increase Accuracy Training Speed and Cost Optimization Inference Speed and Cost Optimization Scale" Creating Problem-Solving Agents using GenAI for Action Composition,"Creating Problem-Solving Agents using GenAI for Action Composition 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. 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" Improving Real World RAG Systems: Key Challenges & Practical Solutions,"Improving Real World RAG Systems: Key Challenges & Practical Solutions 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. 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" Framework to Choose the Right LLM for your Business,"Framework to Choose the Right LLM for your Business 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. 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" Building Smarter LLMs with Mamba and State Space Model,"Building Smarter LLMs with Mamba and State Space Model 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. 1 Course Overview Course Overview 2 An Alternative to Transformers 3 Understanding State Space Models 4 Mamba - A Selective State Space Model" Generative AI - A Way of Life - Free Course,"Generative AI - A Way of Life - Free Course 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. 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. Getting Started with Large Language Models Building LLM Applications Using Prompt Engineering Building your First RAG System using LlamaIndex 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 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" Building LLM Applications using Prompt Engineering - Free Course,"Building LLM Applications using Prompt Engineering - Free Course 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. 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" Building Your First Computer Vision Model - Free Course,"Building Your First Computer Vision Model - Free Course 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. 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" Bagging and Boosting ML Algorithms - Free Course,"Bagging and Boosting ML Algorithms - Free Course 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 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" MidJourney: From Inspiration to Implementation - Free Course,"MidJourney: From Inspiration to Implementation - Free Course 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. 1 MidJourney MidJourney - Storm _ Story MidJourney - Inspiration MidJourney - How to use MidJourney Alternatives Quiz" Understanding Linear Regression - Free Course,"Understanding Linear Regression - Free Course This free course will help you understand the fundamentals of linear regression in a straightforward manner. By the end of this course, you will be able to build predictive models using linear regression techniques. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering linear regression. Professionals: Individuals looking to expand their skill set and apply machine learning across different industries. Aspiring Students: For those setting out on their journey to mastering machine learning and making a mark in the tech world. 1 Linear Regression Introduction to the Problem Statement Resources for this Course Introduction to Linear Regression Significance of Slope and Intercept in the linear regression How Model Decides The Best-Fit Line Let’s Build a Simple Linear Regression Model Model Understanding Using Descriptive Approach Model Understanding Using Descriptive Approach - II Model Building Using Predictive Approach Quiz: Linear regression" The Working of Neural Networks - Free Course,"The Working of Neural Networks - Free Course This free course will help you understand the end-to-end working of neural networks in a simple manner. By the end of this course, you will be able to build advanced Deep Learning models using the PyTorch framework. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering deep learning. It is recommended that you complete the advanced Machine Learning course before taking up this course. Professionals: Individuals looking to expand their skill set and apply deep learning across different industries. Aspiring Students: For those setting out on their journey to mastering deep learning and making a mark in the tech world. 1 Understanding the working of Neural Networks How are Neural Networks trained - Forward Propagation Understanding Loss Functions + Hands on Reading: Creating a Custom Loss Function (Optional) Optimization Techniques - Gradient Descent What is Back Propagation? Types of Gradient Descent Common Optimization Techniques - Part 1 Common Optimization Techniques - Part 2 Building a Deep Neural Network (Hands-on Regression Model) Building a Deep Neural Network (Hands-on Classification Model) Quiz" The A to Z of Unsupervised ML - Free Course,"The A to Z of Unsupervised ML - Free Course Unsupervised machine learning helps uncover hidden patterns and structures in data without labeled examples. It is essential for exploratory data analysis, reducing dimensionality, and discovering intrinsic relationships within datasets. Mastering unsupervised techniques enhances data preprocessing and drives insights in complex datasets where labels are scarce or unavailable. Professionals: Individuals looking to expand their skill set and apply unsupervised learning across different industries. Aspiring Students: For those setting out on their journey to mastering machine learning and making a mark in the tech world. 1 Understanding Unsupervised Machine Learning Resources to be used in this course. Setting the Context Choosing Clustering Algorithms Solving our Problem using k-means - Part 1 Solving our Problem using k-means - Part 2 Finding optimal K value Analysis and Insights Based on the Plots Introduction to Hierarchical Clustering Analysis (HCA) Solving our Problem using Hierarchical Clustering Introduction to DBSCAN Clustering Solving our Problem using DBSCAN Reading: Applications of Clustering in the Real World Project" Building Your first RAG System using LlamaIndex - Free Course,"Building Your first RAG System using LlamaIndex - Free Course This course will guide you through building your first Retrieval-Augmented Generation (RAG) system using LlamaIndex. You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively. 1 Introduction to RAG systems Welcome to this course Why RAG What is RAG system Overview of RAG Framework Quiz Course handouts 2 Getting Started with LlamaIndex" Data Preprocessing on a Real-World Problem Statement - Free Course,"Data Preprocessing on a Real-World Problem Statement - Free Course This course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. By the end of the course, you will have mastered techniques like EDA and Missing Value Treatment. Who Should Enroll: Professionals: Individuals looking to expand their skill set on data cleaning and preparation. Aspiring Students: For those setting out on their journey to become a data scientist and making a mark in the tech world. 1 Preparing the Dataset for Machine Learning Model Resources to be used in this course Introduction to Problem Statement Reading Material - Understanding the Data ML-workflow Tasks to be Performed Combining Product Attribute Data with POS Data Combining all the tables in the Dataframe Understanding the Combined Data Treating Missing Values - Part 1 Treating Missing Values Part - 2 Outlier Detection and Treatment Preparing the Dataset for Supervised and Unsupervised Models Generative AI for Data Analysis" Exploring Stability.AI - Free Course,"Exploring Stability.AI - Free Course This course will give you a practical understanding of Stability.AI tools. By the end of the course, you will be able to deploy and customize SD WebUI, and use the Automatic1111 WebUI on RunPod GPU environments. You'll learn to install, set up, generate, and fine-tune SD WebUI settings, equipping you with the skills to harness Stability.AI's full potential for your projects. Who Should Enroll: Professionals: Individuals aiming to enhance their skill set and apply Stability.AI tools/Stable Diffusion in various fields. Aspiring Students: Those beginning their journey to mastering Generative AI tool deployment and customization, looking to make an impact in the evolving world of Generative AI 1 Mastering stability.ai and its tools Introduction to Stability How to use Stability.AI tools Review of Deployment Options for SD WebUI Automatic1111 WebUI on RunPod GPU environment SD WebUI Hands-On - Installation and Setup SD WebUI Hands-On - Generation and Settings Quiz" Building a Text Classification Model with Natural Language Processing - Free Course,"Building a Text Classification Model with Natural Language Processing - Free Course Gain practical insights into Natural Language Processing (NLP) with our comprehensive course. Learn to build NLP models using PyTorch, delve into classification models, and apply techniques like bag-of-words, count vectorizer and so on. Perfect for professionals seeking to enhance their skills and aspiring students entering the tech industry. Who Should Enroll: Professionals: Expand your skill set with NLP for real-world applications in diverse industries. Aspiring Students: Master text data analysis and kickstart your career in AI and NLP. 1 Introduction to NLP What is NLP Common tasks in a NLP Project NLP Libraries Resources for the Course Methods of Text Preprocessing - Part 1 Methods of Text Preprocessing - Part 2 Methods of Text Preprocessing - Part 3 Quiz 2 Building a basic classification model" Getting Started with Large Language Models,"Getting Started with Large Language Models This course will help you gain a comprehensive understanding of Large Language Models (LLMs) and develop advanced natural language processing (NLP) applications using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming an expert in LLMs. Master the techniques to build and fine-tune LLMs, and generate human-like text. Who Should Enroll: Professionals: Individuals looking to expand their skill set and leverage LLMs across different industries. Aspiring Students: For those setting out on their journey to master language data analysis and leave a mark in the tech world. 1 Introduction Course Objective Course Handouts The Exponential Growth 2 The Evolution of NLP 3 What are Large Language Models? 4 The Current State of the Art in LLMs 5 Generative AI - Glossary 6 Your Feedback Matters!" Introduction to Generative AI,"Introduction to Generative AI This course will provide you with a comprehensive understanding of generative AI, including text and image generation techniques. By the end of the course, you will be have an understanding of using generative AI tools to create diverse content. You'll learn how generative AI works, engage in practical exercises, and gain the skills to implement these techniques in real-world projects. Who Should Enroll: Professionals: Individuals looking to enhance their skills in generative AI and apply advanced techniques to create innovative solutions across various domains. Aspiring Students: Individuals eager to enter the field of generative AI and apply generative AI techniques to tackle complex problems and generate creative content across different fields. 1 Introduction to Generative AI What is Generative AI How does Gen AI work Quiz Text Generation with Gen AI Image Generation with Gen AI Quiz Meet your Instructors Course Handout Your Feedback Matters!" Exploring Natural Language Processing (NLP) using Deep Learning,"Exploring Natural Language Processing (NLP) using Deep Learning Natural Language Processing (NLP) is the hottest field in the data science space right now. The demand for NLP experts and data scientists is through the roof. We have seen a remarkable rise in the amount of research and breakthroughs in NLP recently. We can trace this recent rise to one (seismic) paper – “Attention is All You Need” by Google AI. This breakthrough has spawned so many new and exciting NLP libraries that enable us to work with text in ways that were previously limited to our imagination (or Hollywood). These NLP deep learning developments have spawned some mind-boggling applications. We’re sure you’ve already interacted with and used some of them yourself: Human-level chatbots (dialogue systems like Google Duplex and Meena) Text generation (Machines writing text from scrtach, such as OpenAI’s GPT-2 model) Here’s a quick list of the latest Natural Language Processing concepts in deep learning every NLP expert and even enthusiast should know: Language Modeling Sequence-to-Sequence Modeling Attention Mechanism Transformer Architecture Transfer Learning in NLP, and much more! These are key NLP deep learning concepts you should master if you want to successfully navigate through the NLP goldmine. Every once in a while, there comes a library or framework that reshapes and reimagines how we look at the field of deep learning. We can safely say that PyTorch is on that list of deep learning frameworks. It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive (a data scientist’s dream!). You will love working with PyTorch for NLP tasks like building language models and creating deep learning powered chatbots. You will quickly find yourself leaning on PyTorch’s flexibility and efficiency quite often for NLP. Recent Developments in NLP In the past couple of years, the amount of research and breakthroughs in NLP has skyrocketed. Now every month we get to see state-of-the-art NLP models and architectures. This incredible rise of NLP can be traced back to the paper – “Attention is All You Need” by Google AI in June 2017. This breakthrough has spawned so many new and exciting NLP models and some key developments are listed below: Transformer Architecture - It achieved state-of-the-art status on sequence modeling tasks. It can handle long-range dependencies in text with ease BERT - This architecture by Google took the Transformer a notch higher and achieved state-of-the-art performance in Language Modeling GPT and GPT 2 - OpenAI’s GPT was state-of-the-art language model till BERT toppled it from the top spot and soon they launched GPT 2 a colossal language model which was trained on many GB’s of text scraped from the web Transformers Library - This amazing library by Huggingface has democratized the use of Transformers. It has a large number of Transformer based pre-trained models that can also be fine-tuned. In the modules given below, all the projects will be implemented using PyTorch. Language Modeling In this module, you will understand a very important sequence modeling task that is known as language modeling. This is the main ingredient in many modern NLP tasks such as machine translation, text summarization, speech recognition, etc. Project: Building an Autocomplete System In this project, you will train a Language Model from scratch and use it to automatically give next word suggestions to an input text. You will use PyTorch to implement this project. Sequence-to-Sequence Modeling Once you are familiar with the language model, you will learn about the sequence-to-sequence models wherein both the input and the output are sequences of different lengths. You will also get a thorough understanding of the architecture along with the attention mechanism. Project: Neural Machine Translation In this project you will get to apply sequence-to-sequence modeling to build a deep learning model to translate one language to another automatically. Transformers and Transfer Learning in NLP The next step in your journey is the Transformer. In this module, you will understand the Transformers models in detail. You will also learn how Transformers are used to perform transfer learning in NLP. You will get to use the Transformers library by Huggingface. Project: Text Classification using Transformers You will fine-tune a pre-trained Transformer model to perform text classification on a new dataset. The domain of the new dataset will be different from that of the dataset on which the Transformer was trained. Project: Build a Dialog System using RASA Conversational agents or chatbots have been instrumental in across several industries. In this project you will build a chatbot using RASA, an open source. You will also learn to deploy the chatbot in slack. Project: Build a Voice Command System In this project you will not only use text data but audio data as well. The objective of this project is to build a deep learning system to convert a speech input to text. It can be used for tasks such as controlling home automation systems by just a few voice commands. " Getting Started with Deep Learning,"Getting Started with Deep Learning Deep learning algorithms are revolutionizing data science industry and disrupting several domains. From computer vision applications to natural language processing (NLP) use cases - every field is benefitting from use of Deep Learning models. This course starts by assuming no knowledge about Neural Networks and Deep Learning and introduces these subjects to the student one by one. The course helps you build a deep as well as intuitive understanding of what is Deep Learning, where can Deep Learning Models be applied and then helps you solve several real life problems using Keras and PyTorch frameworks. Download Brochure These deep learning algorithms are powered by techniques like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), etc. If you’re a beginner in deep learning, or you are looking to hone your existing deep learning and neural network skills, you must have asked these questions: What is deep learning? What are the different types of neural networks? What part do these neural networks play in the deep learning space? When should you choose convolutional neural networks (CNN) over recurrent neural networks (RNN) - and vice versa? How can you build deep learning models in Python? What is forward propagation? And what is backpropagation? What are the different loss functions in deep learning? What are the different deep learning frameworks? TensorFlow vs. PyTorch vs. Keras - which deep learning framework should you choose? The course is designed to answer all the above questions in an applied first methodology. Loan Eligibility Prediction Classify Emergency Vehicles from Non- Emergency Vehicles Auto Tagging Stack Overflow Queries Web Traffic Forecasting Audio Classification Download Project Details 1 Introduction to Deep Learning What is Deep Learning? FREE PREVIEW Difference b/w Deep Learning and Machine Learning FREE PREVIEW Quiz: Difference between Machine Learning and Deep Learning Why Deep Learning is so popular? FREE PREVIEW Quiz: Why Deep learning is so popular? 2 Getting ready for the course 3 Introduction to the course 4 Introduction to Neural Network 5 Forward and Backward Propagation 6 Activation Functions 7 Optimizers 8 Loss Function 9 NN on structured Data 10 Assignment: Big Mart Sales Prediction 11 Functional API in Keras for Deep Learning 12 Getting started with image data 13 Solving Image Classification Using Keras 14 Assignment: Gender Classification 15 Improving your Deep Learning Model 16 Introduction to Convolutional Neural Network and Implementation 17 Introduction to Transfer Learning 18 CNN Visualization 19 Real World Use Cases of Deep Learning 20 Working with Text Data 21 Getting Started with Recurrent Neural Networks (RNN) 22 Project - Building an Auto-Tagging System 23 Advanced Sequence Models - LSTM & GRU 24 Assignment: Identify the Sentiments 25 Project - Web Traffic Forecasting 26 Project - Emergency vs Non Emergency Vehicle Sound Classification 27 Assignment: Urban Sound Classification 28 Unsupervised Deep Learning 29 Assignment: Image Denoising 30 Introduction to PyTorch" Nano Course: Dreambooth-Stable Diffusion for Custom Images,"Nano Course: Dreambooth-Stable Diffusion for Custom Images Have you ever wondered how to turn your dreams into reality by creating images of your dog traveling around the world or yourself alongside Elon Musk or playing cricket with MSD? This is exactly where the dreambooth model comes into the picture. With the help of Dreambooth, you can personalize the stable diffusion for a particular subject. Given just 5 images of our subject, dreambooth can create new images across diverse scenes, poses, views, and lighting conditions that do not appear in the reference images. In this free nano course on Dreambooth, Sandeep will discuss the historical journey of stable diffusion, its current landscape, and a brief understanding of the stable diffusion training process. Then we will move on to the dreambooth, its training process and finetune dreambooth on our custom dataset. 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" A Comprehensive Learning Path for Deep Learning in 2023,"A Comprehensive Learning Path for Deep Learning in 2023 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. 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. This is a beginner friendly course and has no prerequisites. 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) 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" A Comprehensive Learning Path to Become a Data Scientist in 2024,"A Comprehensive Learning Path to Become a Data Scientist in 2024 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. 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" Nano Course: Building Large Language Models for Code,"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 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" Nano Course: Cutting Edge LLM Tricks,"Nano Course: Cutting Edge LLM Tricks In this free nano GenAI course on Cutting Edge LLM Tricks, you will- Learn latest LLM tricks and techniques from top research papers Apply the LLM tricks in building State-Art-Of-The-Art (SOTA) LLMs Recap of LLM journey, building blocks of LLMs and LLM Stack. 1 Cutting Edge LLM Tricks Introduction Recap of LLMs Building Blocks of LLMs Applying LLMs in Practise LIMA- Less is More Alignment Distil Step by Step Instruction BackTranslation Textbooks are all you Need Sycophancy-Reducing False answers Gorilla- Helping LLMs follow APIs ToolLLMs Summary" Machine Learning Summer Training,"Machine Learning Summer Training This is the second step of the Machine Learning Summer Training, want to know more click here. 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! 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. 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. This course requires no prior knowledge about Data Science or any tool. 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 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" A Comprehensive Learning Path to Become a Data Engineer in 2022,"A Comprehensive Learning Path to Become a Data Engineer in 2022 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. This is a beginner-friendly course and has no prerequisites. 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" Writing Powerful Data Science Articles,"Writing Powerful Data Science Articles ""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! 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 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..." Machine Learning Certification Course for Beginners,"Machine Learning Certification Course for Beginners 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. This course requires no prior knowledge of Data Science or any tool. 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" Data Science Career Conclave,"Data Science Career Conclave 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? 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)" Top Data Science Projects for Analysts and Data Scientists,"Top Data Science Projects for Analysts and Data Scientists 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! 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" Getting Started with Git and GitHub for Data Science Professionals,"Getting Started with Git and GitHub for Data Science Professionals 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! 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?" "Data Science Hacks, Tips and Tricks","Data Science Hacks, Tips and Tricks 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! 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! 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! 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" Introduction to Business Analytics,"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 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 5 Your Feedback Matters!" Introduction to PyTorch for Deep Learning,"Introduction to PyTorch for Deep Learning 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. 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? 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. 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" Introduction to Natural Language Processing,"Introduction to Natural Language Processing 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. 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. 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 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?" Getting started with Decision Trees,"Getting started with Decision Trees 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. 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? 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 Where to go from here? AI&ML Blackbelt Plus Program (Sponsored)" Introduction to Python,"Introduction to Python 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 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? 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. 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! 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. 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?" Loan Prediction Practice Problem (Using Python),"Loan Prediction Practice Problem (Using Python) 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 This course assumes that you have familiarity with Python. 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)" Big Mart Sales Prediction Using R,"Big Mart Sales Prediction Using R 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. This course assumes that you have familiarity with R. 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)" Twitter Sentiment Analysis,"Twitter Sentiment Analysis 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. 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)" Pandas for Data Analysis in Python,"Pandas for Data Analysis in Python 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. 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. 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! 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" Support Vector Machine (SVM) in Python and R,"Support Vector Machine (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. 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! 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! 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" Evaluation Metrics for Machine Learning Models,"Evaluation Metrics for 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! 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? 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. 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. 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?" Fundamentals of Regression Analysis,"Fundamentals of Regression Analysis 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. 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? 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. 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?" Getting Started with scikit-learn (sklearn) for Machine Learning,"Getting Started with scikit-learn (sklearn) 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. 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! 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 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 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. 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..." Convolutional Neural Networks (CNN) from Scratch,"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 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?" Dimensionality Reduction for Machine Learning,"Dimensionality Reduction for Machine Learning 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! 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 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. 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" K-Nearest Neighbors (KNN) Algorithm in Python and R,"K-Nearest Neighbors (KNN) Algorithm in Python and R 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. 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! 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 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 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. 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?" Ensemble Learning and Ensemble Learning Techniques,"Ensemble Learning and Ensemble Learning Techniques 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! - 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! 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 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! 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?" Linear Programming for Data Science Professionals,"Linear Programming for Data Science Professionals 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. 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! 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! 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" Naive Bayes from Scratch,"Naive Bayes from Scratch 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 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. 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?" Learn Swift for Data Science,"Learn Swift for Data Science 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! 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: 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! 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! 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?" Introduction to Web Scraping using Python,"Introduction to 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! 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! 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! 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" Tableau for Beginners,"Tableau for Beginners 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! 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! 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?" Getting Started with Neural Networks,"Getting Started with Neural Networks 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. 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?" Introduction to AI & ML,"Introduction to AI & 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! 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" Winning Data Science Hackathons - Learn from Elite Data Scientists,"Winning Data Science Hackathons - Learn from Elite Data Scientists 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! A working knowledge of basic machine learning algorithms will help you understand the talks covered in this 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. 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" Hypothesis Testing for Data Science and Analytics,"Hypothesis Testing for Data Science and Analytics 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. A basic knowledge of descriptive statistics like - mean, median, mode, variance and standard deviation. Experience in Python is a plus! 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"