| {"id": "ct_001", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? For numerical data? = Wyoulube - Search Q q alles Wel eT TT Lowest 155 No | 180 Yes Step 1) Sort the patients by weight, lowest to highest. 190 No 220 Yes Highest 225 Yes S61 #statqu", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? For numerical data? = Wyoulube - Search Q q alles Wel eT TT Lowest 155 No | 180 Yes Step 1) Sort the patients by weight, lowest to highest. 190 No 220 Yes Highest 225 Yes S61 #statqu", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_002", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? So essentially a decision tree asks a question and classifies the person. This decision tree is based on a yes / no question.", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? So essentially a decision tree asks a question and classifies the person. This decision tree is based on a yes / no question.", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_003", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Agenda\nContent type: Visual lecture\n---\nAgenda Assumptions Introduction The basics: What, Why and How? Understanding the concept Practical Implementation", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Agenda\nContent type: Visual lecture\n---\nAgenda Assumptions Introduction The basics: What, Why and How? Understanding the concept Practical Implementation", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_004", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Pruning?\nContent type: Visual lecture\n---\nPruning? Pruning helps avoid overfitting by cutting nodes or branches that add little value to the tree. Pre-pruning: stop growing the tree early, removing a node during construction if it looks unimp", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Pruning?\nContent type: Visual lecture\n---\nPruning? Pruning helps avoid overfitting by cutting nodes or branches that add little value to the tree. Pre-pruning: stop growing the tree early, removing a node during construction if it looks unimp", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_005", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? Comparing features one by one", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? Comparing features one by one", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_006", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Jargons\nContent type: Visual lecture\n---\nJargons root node decision nodes salary at least $50,000 Decision Tree: Should | accept a new job offer? leaf nodes", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Jargons\nContent type: Visual lecture\n---\nJargons root node decision nodes salary at least $50,000 Decision Tree: Should | accept a new job offer? leaf nodes", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_007", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Why are we interested in it?\nContent type: Visual lecture\n---\nWhy are we interested in it? It’s visual and therefore easy Helps businesses relate and validate the rules / conditions Intuitive Works well with numbers and categories both Less data cleaning require", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Why are we interested in it?\nContent type: Visual lecture\n---\nWhy are we interested in it? It’s visual and therefore easy Helps businesses relate and validate the rules / conditions Intuitive Works well with numbers and categories both Less data cleaning require", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_008", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? It can be a bit longer too ☺ Remember trees come in all shapes and sizes. Am I hungry? Yes No Sleep / Work Desert? Main Course? Mango Pudding Ice cream Steak Salad", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? It can be a bit longer too ☺ Remember trees come in all shapes and sizes. Am I hungry? Yes No Sleep / Work Desert? Main Course? Mango Pudding Ice cream Steak Salad", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_009", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? On a serious note Let’s simplify Does the person like BTS songs? Yes No The person is a little less awesome ;) The person is Awesome!", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? On a serious note Let’s simplify Does the person like BTS songs? Yes No The person is a little less awesome ;) The person is Awesome!", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_010", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? 0 @ wr p 5a 4 DS National Schedule: 2020 x + StatQuest: Decision Tre «1 0 Ce 3 Cc Peprertsekes Merely, = Wvoulube “ Search Q Sleep Better All we have left is Chest Pain, so first we'", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? 0 @ wr p 5a 4 DS National Schedule: 2020 x + StatQuest: Decision Tre «1 0 Ce 3 Cc Peprertsekes Merely, = Wvoulube “ Search Q Sleep Better All we have left is Chest Pain, so first we'", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_011", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? Have you driven? Have you cooked? Have you thought? Anticipation / What – If? In milliseconds Let’s meet my friend Bob.", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: What is it?\nContent type: Visual lecture\n---\nWhat is it? Have you driven? Have you cooked? Have you thought? Anticipation / What – If? In milliseconds Let’s meet my friend Bob.", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_012", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? = Wyoulube - Search Heart Disease Heart Disease AC No 6 76 Gini impurity for Chest Pain = 0.29 The Gini impurity for this node, before using chest pain to separate patients is... Blo", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? = Wyoulube - Search Heart Disease Heart Disease AC No 6 76 Gini impurity for Chest Pain = 0.29 The Gini impurity for this node, before using chest pain to separate patients is... Blo", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Advanced\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_013", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? = Wvoulube - Search Q Heart Disease Heart Disease CS i} CS No alts} 39 cr 125 Gini impurity = 0.395 0.336 ( Gini impurity for Chest Pain = weighted average of Gini impurities for the", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: How does it work?\nContent type: Visual lecture\n---\nHow does it work? = Wvoulube - Search Q Heart Disease Heart Disease CS i} CS No alts} 39 cr 125 Gini impurity = 0.395 0.336 ( Gini impurity for Chest Pain = weighted average of Gini impurities for the", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_014", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Introduction\nContent type: Visual lecture\n---\nIntroduction Bird or animal? How do you identify so quickly? You know the rules!", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Introduction\nContent type: Visual lecture\n---\nIntroduction Bird or animal? How do you identify so quickly? You know the rules!", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_015", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: What if?\nContent type: Visual lecture\n---\nWhat if? What if instead of 1 tree there was a way that multiple tress could work in parallel? Would that not be efficient? Especially with larger data size? Is there a way where imbalance does not im", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: What if?\nContent type: Visual lecture\n---\nWhat if? What if instead of 1 tree there was a way that multiple tress could work in parallel? Would that not be efficient? Especially with larger data size? Is there a way where imbalance does not im", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_016", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Resources / References\nContent type: Visual lecture\n---\nResources / References https://mychartguide.com/decision-tree/ https://statquest.com https://aionlinecourse.com", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Resources / References\nContent type: Visual lecture\n---\nResources / References https://mychartguide.com/decision-tree/ https://statquest.com https://aionlinecourse.com", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_017", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: To summarize\nContent type: Visual lecture\n---\nTo summarize You start at the top Keep going down Until you can’t anymore", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: To summarize\nContent type: Visual lecture\n---\nTo summarize You start at the top Keep going down Until you can’t anymore", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_018", "title": "Decision Trees Revised.pptx", "context": "File: Decision Trees Revised.pptx\nSection: Many algorithms\nContent type: Visual lecture\n---\nMany algorithms Decision Trees Renamed as CART ID3 ID4.5 C5.0 Etc.", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Decision Trees Revised.pptx\nSection: Many algorithms\nContent type: Visual lecture\n---\nMany algorithms Decision Trees Renamed as CART ID3 ID4.5 C5.0 Etc.", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_019", "title": "HandsOn1_Window_Functions.md", "context": "File: HandsOn1_Window_Functions.md\nSection: Hands-On 1 - Window Functions\nContent type: Practical lab\n---\nCreate Database/Tables ********************************/ -- Create the windowdb database if it doesn't exist CREATE DATABASE IF NOT EXISTS windowdb; USE windowdb; -- Create a sales table if it doesn't", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: HandsOn1_Window_Functions.md\nSection: Hands-On 1 - Window Functions\nContent type: Practical lab\n---\nCreate Database/Tables ********************************/ -- Create the windowdb database if it doesn't exist CREATE DATABASE IF NOT EXISTS windowdb; USE windowdb; -- Create a sales table if it doesn't", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"Window Functions\"}"], "answer_start": [0]}} |
| {"id": "ct_020", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Summary\nContent type: Visual lecture\n---\nPandas A Python library for working with structured data. Series One-dimensional Single column Values + Index DataFrame Two-dimensional Multiple columns Collection of Series Summary", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Summary\nContent type: Visual lecture\n---\nPandas A Python library for working with structured data. Series One-dimensional Single column Values + Index DataFrame Two-dimensional Multiple columns Collection of Series Summary", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_021", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Every Series Has an Index and Values\nContent type: Visual lecture\n---\nEvery Series Has an Index and Values Pandas Series: What ages.index # RangeIndex(start=0, stop=3, step=1) ages.values # array([22, 24, 21]) Default index Stored data values Let’s Define a series: ages", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Every Series Has an Index and Values\nContent type: Visual lecture\n---\nEvery Series Has an Index and Values Pandas Series: What ages.index # RangeIndex(start=0, stop=3, step=1) ages.values # array([22, 24, 21]) Default index Stored data values Let’s Define a series: ages", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_022", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: The Index Is Also an Object\nContent type: Visual lecture\n---\nThe Index Is Also an Object Pandas: Index Pandas stores row labels in a separate Index object. obj = pd.Series(range(3), index=[\"a\", \"b\", \"c\"]) obj.index # Index([\"a\", \"b\", \"c\"], dtype=\"object\") This ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: The Index Is Also an Object\nContent type: Visual lecture\n---\nThe Index Is Also an Object Pandas: Index Pandas stores row labels in a separate Index object. obj = pd.Series(range(3), index=[\"a\", \"b\", \"c\"]) obj.index # Index([\"a\", \"b\", \"c\"], dtype=\"object\") This ", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_023", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: When Should You Use a Series?\nContent type: Visual lecture\n---\nWhen Should You Use a Series? Pandas Series: When Use a Series when you are focusing on one column, feature, or measurement. Analyze one column Age, score, salary, price Calculate statistics mean(), m", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: When Should You Use a Series?\nContent type: Visual lecture\n---\nWhen Should You Use a Series? Pandas Series: When Use a Series when you are focusing on one column, feature, or measurement. Analyze one column Age, score, salary, price Calculate statistics mean(), m", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_024", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Index Labels Are Immutable\nContent type: Visual lecture\n---\nIndex Labels Are Immutable Pandas: Index You can replace the whole index, but you cannot edit one label in place. index = pd.Index([\"a\", \"b\", \"c\"]) index[1:] # Index([\"b\", \"c\"], dtype=\"object\") Allowe", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Index Labels Are Immutable\nContent type: Visual lecture\n---\nIndex Labels Are Immutable Pandas: Index You can replace the whole index, but you cannot edit one label in place. index = pd.Index([\"a\", \"b\", \"c\"]) index[1:] # Index([\"b\", \"c\"], dtype=\"object\") Allowe", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_025", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: DataFrame\nContent type: Visual lecture\n---\nA dataframe is: Think Of It As Excel Spreadsheet SQL Table Student Records DataFrame Pandas DataFrame: What | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | Ahmed | 21 | Damma", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: DataFrame\nContent type: Visual lecture\n---\nA dataframe is: Think Of It As Excel Spreadsheet SQL Table Student Records DataFrame Pandas DataFrame: What | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | Ahmed | 21 | Damma", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_026", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Two core structures\nContent type: Visual lecture\n---\nPandas provides two fundamental structures: Two core structures Data Structure DataFrame ID Name Age City S1 Abdullah 22 Riyadh S2 Sara 24 Jeddah S3 Ahmed 21 Dammam Full table: multiple related column", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Two core structures\nContent type: Visual lecture\n---\nPandas provides two fundamental structures: Two core structures Data Structure DataFrame ID Name Age City S1 Abdullah 22 Riyadh S2 Sara 24 Jeddah S3 Ahmed 21 Dammam Full table: multiple related column", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_027", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: What Is an Index?\nContent type: Visual lecture\n---\nWhat Is an Index? Pandas: Index An index identifies rows or values. Pandas creates one automatically if we do not provide it. | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: What Is an Index?\nContent type: Visual lecture\n---\nWhat Is an Index? Pandas: Index An index identifies rows or values. Pandas creates one automatically if we do not provide it. | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | ", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_028", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Default Index vs Custom Index\nContent type: Visual lecture\n---\nDefault Index vs Custom Index Pandas: Index Custom labels can make row access easier to understand. df.index # RangeIndex(start=0, stop=3, step=1) Index | Name | Age | City 0 | Abdullah | 22 | Riyadh ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Default Index vs Custom Index\nContent type: Visual lecture\n---\nDefault Index vs Custom Index Pandas: Index Custom labels can make row access easier to understand. df.index # RangeIndex(start=0, stop=3, step=1) Index | Name | Age | City 0 | Abdullah | 22 | Riyadh ", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_029", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Why not just use Python lists?\nContent type: Visual lecture\n---\nImagine you are managing data for 10,000 students. You now have: 10,000 names 10,000 ages 10,000 cities All stored separately. As datasets grow, managing separate lists becomes difficult and error-pro", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Why not just use Python lists?\nContent type: Visual lecture\n---\nImagine you are managing data for 10,000 students. You now have: 10,000 names 10,000 ages 10,000 cities All stored separately. As datasets grow, managing separate lists becomes difficult and error-pro", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_030", "title": "Introduction to Pandas_.pptx", "context": "File: Introduction to Pandas_.pptx\nSection: Simple DataFrame Statistics\nContent type: Visual lecture\n---\nSimple DataFrame Statistics Pandas DataFrame: Example Pandas can answer table questions with short commands. | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | Ahmed | 21 | Damm", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Introduction to Pandas_.pptx\nSection: Simple DataFrame Statistics\nContent type: Visual lecture\n---\nSimple DataFrame Statistics Pandas DataFrame: Example Pandas can answer table questions with short commands. | Name | Age | City 0 | Abdullah | 22 | Riyadh 1 | Sara | 24 | Jeddah 2 | Ahmed | 21 | Damm", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_031", "title": "Lecture - Decision Trees.ipynb", "context": "File: Lecture - Decision Trees.ipynb\nSection: 2. EXERCISE\nContent type: Code notebook\n---\n[markdown] ## 2. EXERCISE 1. **Tame a tree.** Grow a single `DecisionTreeRegressor` on Ames with `max_depth=None`, then again with `max_depth=4`. Compare train vs test R² for each. Which overfits more", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture - Decision Trees.ipynb\nSection: 2. EXERCISE\nContent type: Code notebook\n---\n[markdown] ## 2. EXERCISE 1. **Tame a tree.** Grow a single `DecisionTreeRegressor` on Ames with `max_depth=None`, then again with `max_depth=4`. Compare train vs test R² for each. Which overfits more", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"], "answer_start": [0]}} |
| {"id": "ct_032", "title": "Lecture - Pandas Basics.ipynb", "context": "File: Lecture - Pandas Basics.ipynb\nSection: 4. Filtering with conditions\nContent type: Code notebook\n---\n[markdown] ## 4. Filtering with conditions A comparison on a column returns a boolean Series. Passing it back into the DataFrame keeps only rows where the condition is True. This is the same boolean m", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture - Pandas Basics.ipynb\nSection: 4. Filtering with conditions\nContent type: Code notebook\n---\n[markdown] ## 4. Filtering with conditions A comparison on a column returns a boolean Series. Passing it back into the DataFrame keeps only rows where the condition is True. This is the same boolean m", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_033", "title": "Lecture - Pandas Basics.ipynb", "context": "File: Lecture - Pandas Basics.ipynb\nSection: 3. Selecting columns and rows\nContent type: Code notebook\n---\n[markdown] ## 3. Selecting columns and rows Two accessors do most selection: - `.loc[rows, cols]` selects by **label**. - `.iloc[rows, cols]` selects by **integer position**. Pick columns by passing a", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture - Pandas Basics.ipynb\nSection: 3. Selecting columns and rows\nContent type: Code notebook\n---\n[markdown] ## 3. Selecting columns and rows Two accessors do most selection: - `.loc[rows, cols]` selects by **label**. - `.iloc[rows, cols]` selects by **integer position**. Pick columns by passing a", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_034", "title": "Lecture - Pandas Basics.ipynb", "context": "File: Lecture - Pandas Basics.ipynb\nSection: 1. Series and DataFrame\nContent type: Code notebook\n---\n[markdown] ## 1. Series and DataFrame A **Series** is a single column: values plus an index (the row labels). A **DataFrame** is a set of Series sharing one index, so it is a full table. Pulling one c", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture - Pandas Basics.ipynb\nSection: 1. Series and DataFrame\nContent type: Code notebook\n---\n[markdown] ## 1. Series and DataFrame A **Series** is a single column: values plus an index (the row labels). A **DataFrame** is a set of Series sharing one index, so it is a full table. Pulling one c", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_035", "title": "Lecture - Pandas Basics.ipynb", "context": "File: Lecture - Pandas Basics.ipynb\nSection: 2. First look at any dataset\nContent type: Code notebook\n---\n[markdown] ## 2. First look at any dataset Before analysis, always inspect. These four calls answer \"what am I holding?\" - `.shape`: rows and columns - `.head()` / `.tail()`: a few rows - `.info()`: c", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture - Pandas Basics.ipynb\nSection: 2. First look at any dataset\nContent type: Code notebook\n---\n[markdown] ## 2. First look at any dataset Before analysis, always inspect. These four calls answer \"what am I holding?\" - `.shape`: rows and columns - `.head()` / `.tail()`: a few rows - `.info()`: c", "answers": {"text": ["{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"], "answer_start": [0]}} |
| {"id": "ct_036", "title": "Lecture_ML_Workflow.ipynb", "context": "File: Lecture_ML_Workflow.ipynb\nSection: Recap\nContent type: Code notebook\n---\n[markdown] ## Recap - The ML workflow is a fixed loop: frame, prepare, split, choose, fit, predict, evaluate, iterate. It applies whether the target is a number or a category. - A held out test set is", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture_ML_Workflow.ipynb\nSection: Recap\nContent type: Code notebook\n---\n[markdown] ## Recap - The ML workflow is a fixed loop: frame, prepare, split, choose, fit, predict, evaluate, iterate. It applies whether the target is a number or a category. - A held out test set is", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_037", "title": "Lecture_ML_Workflow.ipynb", "context": "File: Lecture_ML_Workflow.ipynb\nSection: 4. Model: Linear Regression\nContent type: Code notebook\n---\n[markdown] ## 4. Model: Linear Regression **Linear Regression** learns a weighted combination of the features that best predicts the target. It is fast, and its coefficients are directly readable as \"", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture_ML_Workflow.ipynb\nSection: 4. Model: Linear Regression\nContent type: Code notebook\n---\n[markdown] ## 4. Model: Linear Regression **Linear Regression** learns a weighted combination of the features that best predicts the target. It is fast, and its coefficients are directly readable as \"", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_038", "title": "Lecture_ML_Workflow.ipynb", "context": "File: Lecture_ML_Workflow.ipynb\nSection: Note\nContent type: Code notebook\n---\n[markdown] ## Note If you feel like some ML terms such as confusion matrix, precision, recall, train data, test data, etc. are difficult to understand, do not worry. Just hold on to these terms, note ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture_ML_Workflow.ipynb\nSection: Note\nContent type: Code notebook\n---\n[markdown] ## Note If you feel like some ML terms such as confusion matrix, precision, recall, train data, test data, etc. are difficult to understand, do not worry. Just hold on to these terms, note ", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_039", "title": "Lecture_ML_Workflow.ipynb", "context": "File: Lecture_ML_Workflow.ipynb\nSection: 7. Evaluate: measure performance on data the model never saw\nContent type: Code notebook\n---\n[markdown] ## 7. Evaluate: measure performance on data the model never saw Regression used MAE, RMSE, and R-squared. Classification needs different metrics, a prediction is either right or wrong, ther", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture_ML_Workflow.ipynb\nSection: 7. Evaluate: measure performance on data the model never saw\nContent type: Code notebook\n---\n[markdown] ## 7. Evaluate: measure performance on data the model never saw Regression used MAE, RMSE, and R-squared. Classification needs different metrics, a prediction is either right or wrong, ther", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_040", "title": "Lecture_ML_Workflow.ipynb", "context": "File: Lecture_ML_Workflow.ipynb\nSection: 5. Look at the errors, not just the summary numbers\nContent type: Code notebook\n---\n[markdown] ## 5. Look at the errors, not just the summary numbers A single error number can hide a lot. Plotting predicted versus actual values shows where the model struggles: does it consistently un", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Lecture_ML_Workflow.ipynb\nSection: 5. Look at the errors, not just the summary numbers\nContent type: Code notebook\n---\n[markdown] ## 5. Look at the errors, not just the summary numbers A single error number can hide a lot. Plotting predicted versus actual values shows where the model struggles: does it consistently un", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_041", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: 3. Explore Data\nContent type: Visual lecture\n---\nX (Feature) Y (Label) Data Exploration 3. Explore Data Understanding the ML Workflow", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: 3. Explore Data\nContent type: Visual lecture\n---\nX (Feature) Y (Label) Data Exploration 3. Explore Data Understanding the ML Workflow", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_042", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Slide 23\nContent type: Visual lecture\n---\nKNN Model X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 6 - generate prediction predictions = knn.predict(X_test) 6. Prediction on Test", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Slide 23\nContent type: Visual lecture\n---\nKNN Model X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 6 - generate prediction predictions = knn.predict(X_test) 6. Prediction on Test", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_043", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Interpretation - Feature Importance\nContent type: Visual lecture\n---\nInterpretation - Feature Importance Understanding the ML Workflow Data Collection Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Validation", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Interpretation - Feature Importance\nContent type: Visual lecture\n---\nInterpretation - Feature Importance Understanding the ML Workflow Data Collection Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Validation", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_044", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Modeling: Decision Tree\nContent type: Visual lecture\n---\nModeling: Decision Tree Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Modeling: Decision Tree\nContent type: Visual lecture\n---\nModeling: Decision Tree Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_045", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Let’s go through the workflow again, with code demo\nContent type: Visual lecture\n---\nLet’s go through the workflow again, with code demo Understanding the ML Workflow Machine Learning Process - Beginner", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Let’s go through the workflow again, with code demo\nContent type: Visual lecture\n---\nLet’s go through the workflow again, with code demo Understanding the ML Workflow Machine Learning Process - Beginner", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_046", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Model Validation\nContent type: Visual lecture\n---\nSource: Overfitting and methods of addressing it (link) Data Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understandi", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Model Validation\nContent type: Visual lecture\n---\nSource: Overfitting and methods of addressing it (link) Data Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understandi", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_047", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Feature Engineering\nContent type: Visual lecture\n---\nPlant Harvest Process Grading Roast Grind Brew Latte https://bradleyboehmke.github.io/HOML/engineering.html Feature Engineering Understanding the ML Workflow Data gets turned into features which are t", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Feature Engineering\nContent type: Visual lecture\n---\nPlant Harvest Process Grading Roast Grind Brew Latte https://bradleyboehmke.github.io/HOML/engineering.html Feature Engineering Understanding the ML Workflow Data gets turned into features which are t", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_048", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: 4. Split Data into Train/Test\nContent type: Visual lecture\n---\nX (Feature) Y Train Test (Label) Data Exploration 4. Split Data into Train/Test Understanding the ML Workflow", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: 4. Split Data into Train/Test\nContent type: Visual lecture\n---\nX (Feature) Y Train Test (Label) Data Exploration 4. Split Data into Train/Test Understanding the ML Workflow", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_049", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Slide 25\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data # step 8 - load new data iris_new = pd.read_csv('iris_new.csv') 8. Read New Data", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Slide 25\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data # step 8 - load new data iris_new = pd.read_csv('iris_new.csv') 8. Read New Data", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_050", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: 2. Label Data\nContent type: Visual lecture\n---\nX (Feature) Y (Label) 2. Label Data Understanding the ML Workflow", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: 2. Label Data\nContent type: Visual lecture\n---\nX (Feature) Y (Label) 2. Label Data Understanding the ML Workflow", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_051", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Modeling\nContent type: Visual lecture\n---\nPlant Harvest Process Grading Roast Grind Brew Latte Modeling Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration During the m", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Modeling\nContent type: Visual lecture\n---\nPlant Harvest Process Grading Roast Grind Brew Latte Modeling Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration During the m", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_052", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Slide 19\nContent type: Visual lecture\n---\nX (Feature) Y (Label) # step 2 - label data X = iris.loc[:, iris.columns.drop('Species')] y = pd.Series(iris.Species).map({'setosa': 0, 'versicolor':0, 'virginica':1}) .to_frame() .rename(columns={0:'", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Slide 19\nContent type: Visual lecture\n---\nX (Feature) Y (Label) # step 2 - label data X = iris.loc[:, iris.columns.drop('Species')] y = pd.Series(iris.Species).map({'setosa': 0, 'versicolor':0, 'virginica':1}) .to_frame() .rename(columns={0:'", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_053", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Slide 24\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 7 - model evaluation accuracy = sum(predictions == y_test.Species.values) / float(len(y_test)) print (accu", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Slide 24\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 7 - model evaluation accuracy = sum(predictions == y_test.Species.values) / float(len(y_test)) print (accu", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_054", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Modeling: Linear Regression\nContent type: Visual lecture\n---\nModeling: Linear Regression Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Modeling: Linear Regression\nContent type: Visual lecture\n---\nModeling: Linear Regression Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_055", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: 9. Prepare the New Data\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data Prepared 9. Prepare the New Data Understanding the ML Workflow", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: 9. Prepare the New Data\nContent type: Visual lecture\n---\nKNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data Prepared 9. Prepare the New Data Understanding the ML Workflow", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_056", "title": "ML Workflow Introduction.pptx", "context": "File: ML Workflow Introduction.pptx\nSection: Model Validation\nContent type: Visual lecture\n---\nData Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understanding the ML Workflow Learning Curves", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: ML Workflow Introduction.pptx\nSection: Model Validation\nContent type: Visual lecture\n---\nData Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understanding the ML Workflow Learning Curves", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_057", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 13. Further reading\nContent type: Code notebook\n---\n[markdown] ## 13. Further reading - [Anthropic's prompt engineering guide](https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/overview) Practical, current, and organised by technique", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 13. Further reading\nContent type: Code notebook\n---\n[markdown] ## 13. Further reading - [Anthropic's prompt engineering guide](https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/overview) Practical, current, and organised by technique", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_058", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 4. Zero-shot\nContent type: Code notebook\n---\n[markdown] ## 4. Zero-shot **Zero-shot** means asking for the task with no worked examples. Just the instruction. [code] def zero_shot(review): return render(f\"Is the following product review positive", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 4. Zero-shot\nContent type: Code notebook\n---\n[markdown] ## 4. Zero-shot **Zero-shot** means asking for the task with no worked examples. Just the instruction. [code] def zero_shot(review): return render(f\"Is the following product review positive", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_059", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 5. Few-shot\nContent type: Code notebook\n---\n[markdown] ## 5. Few-shot **Few-shot** prompting puts worked examples in the prompt. The model infers the pattern from them, including the output format, without any weight updates. This is why it is ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 5. Few-shot\nContent type: Code notebook\n---\n[markdown] ## 5. Few-shot **Few-shot** prompting puts worked examples in the prompt. The model infers the pattern from them, including the output format, without any weight updates. This is why it is ", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_060", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 1. Setup\nContent type: Code notebook\n---\n[markdown] ## 1. Setup [code] !pip install -q transformers [code] import time import json as jsonlib from pathlib import Path import numpy as np import pandas as pd import matplotlib.pyplot as plt imp", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 1. Setup\nContent type: Code notebook\n---\n[markdown] ## 1. Setup [code] !pip install -q transformers [code] import time import json as jsonlib from pathlib import Path import numpy as np import pandas as pd import matplotlib.pyplot as plt imp", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_061", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 12. Where this sits in practice\nContent type: Code notebook\n---\n[markdown] ## 12. Where this sits in practice **Prompts are hyperparameters, and the harness matters more than the prompt** The single most valuable thing in this notebook is section 3, not any indivi", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 12. Where this sits in practice\nContent type: Code notebook\n---\n[markdown] ## 12. Where this sits in practice **Prompts are hyperparameters, and the harness matters more than the prompt** The single most valuable thing in this notebook is section 3, not any indivi", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_062", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 2. Anatomy of a prompt\nContent type: Code notebook\n---\n[markdown] ## 2. Anatomy of a prompt The single string you type into a chat box is not what the model receives. Instruction-tuned models are trained on a **conversation format** with named roles, and ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 2. Anatomy of a prompt\nContent type: Code notebook\n---\n[markdown] ## 2. Anatomy of a prompt The single string you type into a chat box is not what the model receives. Instruction-tuned models are trained on a **conversation format** with named roles, and ", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_063", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 6. Chain-of-thought\nContent type: Code notebook\n---\n[markdown] ## 6. Chain-of-thought **Chain-of-thought** prompting asks the model to reason step by step before answering. The intermediate tokens give the model somewhere to do work: because it compute", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 6. Chain-of-thought\nContent type: Code notebook\n---\n[markdown] ## 6. Chain-of-thought **Chain-of-thought** prompting asks the model to reason step by step before answering. The intermediate tokens give the model somewhere to do work: because it compute", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_064", "title": "Prompt_Engineering.ipynb", "context": "File: Prompt_Engineering.ipynb\nSection: 3. The evaluation harness, before anything else\nContent type: Code notebook\n---\n[markdown] ## 3. The evaluation harness, before anything else This section comes first on purpose. Almost every prompt engineering tutorial shows a technique, prints one nice-looking output, and moves", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Prompt_Engineering.ipynb\nSection: 3. The evaluation harness, before anything else\nContent type: Code notebook\n---\n[markdown] ## 3. The evaluation harness, before anything else This section comes first on purpose. Almost every prompt engineering tutorial shows a technique, prints one nice-looking output, and moves", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Prompt Engineering\"}"], "answer_start": [0]}} |
| {"id": "ct_065", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: 10. Where this sits in practice\nContent type: Code notebook\n---\n[markdown] ## 10. Where this sits in practice **RAG is the default way to put an LLM on your own data** The alternative is fine-tuning, and for factual knowledge it is usually the wrong tool. RAG upda", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: 10. Where this sits in practice\nContent type: Code notebook\n---\n[markdown] ## 10. Where this sits in practice **RAG is the default way to put an LLM on your own data** The alternative is fine-tuning, and for factual knowledge it is usually the wrong tool. RAG upda", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_066", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: Failure 2: the context was retrieved but ignored\nContent type: Code notebook\n---\n[markdown] ### Failure 2: the context was retrieved but ignored The opposite case: the right information was in the prompt and the model answered from its training data anyway, or contradicted the con", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: Failure 2: the context was retrieved but ignored\nContent type: Code notebook\n---\n[markdown] ### Failure 2: the context was retrieved but ignored The opposite case: the right information was in the prompt and the model answered from its training data anyway, or contradicted the con", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_067", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: 9. Exercise\nContent type: Code notebook\n---\n[markdown] ## 9. Exercise Roughly 15 minutes. The Introduction to Generative AI notebook's exercise built **hybrid search**, combining TF-IDF and embedding scores, and found it beat either alone. Sect", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: 9. Exercise\nContent type: Code notebook\n---\n[markdown] ## 9. Exercise Roughly 15 minutes. The Introduction to Generative AI notebook's exercise built **hybrid search**, combining TF-IDF and embedding scores, and found it beat either alone. Sect", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_068", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: 4. Index and retrieve\nContent type: Code notebook\n---\n[markdown] ## 4. Index and retrieve Embedding the corpus is a one-time cost. Queries are then a matrix multiply, which is why retrieval is fast enough to sit in front of every request. [code] start = ", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: 4. Index and retrieve\nContent type: Code notebook\n---\n[markdown] ## 4. Index and retrieve Embedding the corpus is a one-time cost. Queries are then a matrix multiply, which is why retrieval is fast enough to sit in front of every request. [code] start = ", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_069", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: 1. Setup\nContent type: Code notebook\n---\n[markdown] ## 1. Setup [code] !pip install -q transformers sentence-transformers [code] import time import textwrap from pathlib import Path import numpy as np import pandas as pd import matplotlib.py", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: 1. Setup\nContent type: Code notebook\n---\n[markdown] ## 1. Setup [code] !pip install -q transformers sentence-transformers [code] import time import textwrap from pathlib import Path import numpy as np import pandas as pd import matplotlib.py", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_070", "title": "Retrieval_Augmented_Generation.ipynb", "context": "File: Retrieval_Augmented_Generation.ipynb\nSection: 8. Evaluating a RAG system\nContent type: Code notebook\n---\n[markdown] ## 8. Evaluating a RAG system The essential discipline: **evaluate retrieval and generation separately.** A single end-to-end score tells you the system is bad without telling you which hal", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: Retrieval_Augmented_Generation.ipynb\nSection: 8. Evaluating a RAG system\nContent type: Code notebook\n---\n[markdown] ## 8. Evaluating a RAG system The essential discipline: **evaluate retrieval and generation separately.** A single end-to-end score tells you the system is bad without telling you which hal", "answers": {"text": ["{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"], "answer_start": [0]}} |
| {"id": "ct_071", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: RIGHT JOIN\nContent type: Visual lecture\n---\nRIGHT JOIN 3 OF 4 · EVERY ROW ON THE RIGHT orders customers Shaded = rows returned The mirror of LEFT JOIN. Keeps every row from the right table, and fills in NULL where the left table has no match. S", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: RIGHT JOIN\nContent type: Visual lecture\n---\nRIGHT JOIN 3 OF 4 · EVERY ROW ON THE RIGHT orders customers Shaded = rows returned The mirror of LEFT JOIN. Keeps every row from the right table, and fills in NULL where the left table has no match. S", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_072", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: INSERT\nContent type: Visual lecture\n---\nINSERT DML: ADD A ROW INSERT INTO table_name (col1, col2, ...) VALUES (val1, val2, ...); -- Example INSERT INTO customers (CustomerID, CustomerName, Region) VALUES (10001, 'Aisha Al-Farsi', 'Central')", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: INSERT\nContent type: Visual lecture\n---\nINSERT DML: ADD A ROW INSERT INTO table_name (col1, col2, ...) VALUES (val1, val2, ...); -- Example INSERT INTO customers (CustomerID, CustomerName, Region) VALUES (10001, 'Aisha Al-Farsi', 'Central')", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_073", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 3 Joins & Unions\nContent type: Visual lecture\n---\nMODULE 3 Joins & Unions The training set doesn't live in one table. Joins are how a behavior log, a catalog, and a customer profile become a single modeling dataset.", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 3 Joins & Unions\nContent type: Visual lecture\n---\nMODULE 3 Joins & Unions The training set doesn't live in one table. Joins are how a behavior log, a catalog, and a customer profile become a single modeling dataset.", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_074", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: DELETE\nContent type: Visual lecture\n---\nDELETE DML: REMOVE ROWS DELETE FROM table_name WHERE condition; -- Example DELETE FROM orders WHERE OrderDate < '2009-01-01'; WHAT IT DOES Removes whole rows from a table. WHERE decides which rows are", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: DELETE\nContent type: Visual lecture\n---\nDELETE DML: REMOVE ROWS DELETE FROM table_name WHERE condition; -- Example DELETE FROM orders WHERE OrderDate < '2009-01-01'; WHAT IT DOES Removes whole rows from a table. WHERE decides which rows are", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_075", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 2\nContent type: Visual lecture\n---\nMODULE 2 Aggregations Aggregate to the product level: a feature table ready to feed a pricing or margin model. SELECT ProductID, COUNT(*) AS times_ordered, SUM(Profit) AS total_profit, ROUND(AVG(Disco", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 2\nContent type: Visual lecture\n---\nMODULE 2 Aggregations Aggregate to the product level: a feature table ready to feed a pricing or margin model. SELECT ProductID, COUNT(*) AS times_ordered, SUM(Profit) AS total_profit, ROUND(AVG(Disco", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_076", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: The Superstore Schema\nContent type: Visual lecture\n---\nThe Superstore Schema TODAY'S DATASET Four tables. Think of orders as your user behavior log, and products as your item catalog: exactly the shape of data you'll join for any recommender, churn, or de", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: The Superstore Schema\nContent type: Visual lecture\n---\nThe Superstore Schema TODAY'S DATASET Four tables. Think of orders as your user behavior log, and products as your item catalog: exactly the shape of data you'll join for any recommender, churn, or de", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_077", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: SELECT\nContent type: Visual lecture\n---\nSELECT DML: RETRIEVE DATA SELECT column1, column2, ... FROM table_name WHERE condition; -- Example SELECT CustomerName, Region FROM customers WHERE Region = 'West'; WHAT IT DOES Reads rows from a tabl", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: SELECT\nContent type: Visual lecture\n---\nSELECT DML: RETRIEVE DATA SELECT column1, column2, ... FROM table_name WHERE condition; -- Example SELECT CustomerName, Region FROM customers WHERE Region = 'West'; WHAT IT DOES Reads rows from a tabl", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_078", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 2 Aggregations\nContent type: Visual lecture\n---\nMODULE 2 Aggregations GROUP BY and HAVING turn thousands of transaction rows into the customer- and product-level features a model actually trains on.", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: MODULE 2 Aggregations\nContent type: Visual lecture\n---\nMODULE 2 Aggregations GROUP BY and HAVING turn thousands of transaction rows into the customer- and product-level features a model actually trains on.", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_079", "title": "SQL_Foundations_for_Data_Science.pptx", "context": "File: SQL_Foundations_for_Data_Science.pptx\nSection: UPDATE\nContent type: Visual lecture\n---\nUPDATE DML: CHANGE EXISTING ROWS UPDATE table_name SET column1 = value1, column2 = value2 WHERE condition; -- Example UPDATE orders SET OrderPriority = 'Critical' WHERE Sales > 5000; WHAT IT DOES Chan", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: SQL_Foundations_for_Data_Science.pptx\nSection: UPDATE\nContent type: Visual lecture\n---\nUPDATE DML: CHANGE EXISTING ROWS UPDATE table_name SET column1 = value1, column2 = value2 WHERE condition; -- Example UPDATE orders SET OrderPriority = 'Critical' WHERE Sales > 5000; WHAT IT DOES Chan", "answers": {"text": ["{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"], "answer_start": [0]}} |
| {"id": "ct_080", "title": "WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx", "context": "File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: What is the key difference between traditional programming and machine learning? Correct answer: 2", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: What is the key difference between traditional programming and machine learning? Correct answer: 2", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_081", "title": "WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx", "context": "File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A telecom company wants to flag which customers are likely to cancel their subscription (yes/no). Which ML approach fits best? Correct answer: 2", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A telecom company wants to flag which customers are likely to cancel their subscription (yes/no). Which ML approach fits best? Correct answer: 2", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_082", "title": "WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx", "context": "File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A retail chain wants to predict how many units of a product it will sell next month, a specific number. Which type of ML problem is this? Correct answer: 3", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A retail chain wants to predict how many units of a product it will sell next month, a specific number. Which type of ML problem is this? Correct answer: 3", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_083", "title": "WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx", "context": "File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A new team has almost no historical data and no clear, repeatable pattern for the outcome they want to predict. Should they build an ML model now? Correct answer: 2", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: Scenario: A new team has almost no historical data and no clear, repeatable pattern for the outcome they want to predict. Should they build an ML model now? Correct answer: 2", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_084", "title": "WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx", "context": "File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: A team builds a stacked ensemble combining a random forest, a boosted model, and a linear model, using a meta-model trained on each base model's out-of-fold predictions. The final stack impr", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: A team builds a stacked ensemble combining a random forest, a boosted model, and a linear model, using a meta-model trained on each base model's out-of-fold predictions. The final stack impr", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
| {"id": "ct_085", "title": "WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx", "context": "File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: A data scientist trains a gradient boosting regressor and steadily increases the number of trees while keeping the learning rate fixed. Training error keeps dropping toward zero, but validat", "question": "Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.\n\nFile: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx\nSection: Create a Quiz\nContent type: Quiz\n---\nQuestion: A data scientist trains a gradient boosting regressor and steadily increases the number of trees while keeping the learning rate fixed. Training error keeps dropping toward zero, but validat", "answers": {"text": ["{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Machine Learning Workflow\"}"], "answer_start": [0]}} |
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