id stringlengths 6 6 | title stringclasses 12
values | context stringlengths 141 334 | question stringlengths 252 445 | answers dict |
|---|---|---|---|---|
ct_001 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? For numerical data? = Wyoulube - Search Q q alles Wel eT TT Lowest 155 No | 180 Yes Step 1) Sort ... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_002 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? So essentially a decision tree asks a question and classifies the person. This decision tree is based on a yes / no question. | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? So essentially a decision tree asks a question and classifies the person. This decision tree is based on a ye... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_003 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Agenda
Content type: Visual lecture
---
Agenda Assumptions Introduction The basics: What, Why and How? Understanding the concept Practical Implementation | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Agenda
Content type: Visual lecture
---
Agenda Assumptions Introduction The basics: What, Why and How? Understanding the concept Practical Implementation | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_004 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Pruning?
Content type: Visual lecture
---
Pruning? 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Pruning?
Content type: Visual lecture
---
Pruning? Pruning helps avoid overfitting by cutting nodes or branches that add little value to the tree. Pre-pruning: stop g... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_005 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? Comparing features one by one | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? Comparing features one by one | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_006 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Jargons
Content type: Visual lecture
---
Jargons root node decision nodes salary at least $50,000 Decision Tree: Should | accept a new job offer? leaf nodes | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Jargons
Content type: Visual lecture
---
Jargons root node decision nodes salary at least $50,000 Decision Tree: Should | accept a new job offer? leaf nodes | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_007 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Why are we interested in it?
Content type: Visual lecture
---
Why 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Why are we interested in it?
Content type: Visual lecture
---
Why are we interested in it? It’s visual and therefore easy Helps businesses relate and validate the rul... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_008 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? It can be a bit longer too ☺ Remember trees come in all shapes and sizes. Am I hungry? Yes No Sleep / Work De... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_009 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What 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! | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? On a serious note Let’s simplify Does the person like BTS songs? Yes No The person is a little less awesome ;... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_010 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How 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' | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? 0 @ wr p 5a 4 DS National Schedule: 2020 x + StatQuest: Decision Tre «1 0 Ce 3 Cc Peprertsekes Me... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_011 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? Have you driven? Have you cooked? Have you thought? Anticipation / What – If? In milliseconds Let’s meet my friend Bob. | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: What is it?
Content type: Visual lecture
---
What is it? Have you driven? Have you cooked? Have you thought? Anticipation / What – If? In milliseconds Let’s meet my f... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_012 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? = Wyoulube - Search Heart Disease Heart Disease AC No 6 76 Gini impurity for Chest Pain = 0.29 Th... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Advanced\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_013 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: How does it work?
Content type: Visual lecture
---
How does it work? = Wvoulube - Search Q Heart Disease Heart Disease CS i} CS No alts} 39 cr 125 Gini impurity = 0.3... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_014 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Introduction
Content type: Visual lecture
---
Introduction Bird or animal? How do you identify so quickly? You know the rules! | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Introduction
Content type: Visual lecture
---
Introduction Bird or animal? How do you identify so quickly? You know the rules! | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_015 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: What if?
Content type: Visual lecture
---
What 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: What if?
Content type: Visual lecture
---
What if? What if instead of 1 tree there was a way that multiple tress could work in parallel? Would that not be efficient? ... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_016 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Resources / References
Content type: Visual lecture
---
Resources / References https://mychartguide.com/decision-tree/ https://statquest.com https://aionlinecourse.com | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Resources / References
Content type: Visual lecture
---
Resources / References https://mychartguide.com/decision-tree/ https://statquest.com https://aionlinecourse.co... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_017 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: To summarize
Content type: Visual lecture
---
To summarize You start at the top Keep going down Until you can’t anymore | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: To summarize
Content type: Visual lecture
---
To summarize You start at the top Keep going down Until you can’t anymore | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
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} |
ct_018 | Decision Trees Revised.pptx | File: Decision Trees Revised.pptx
Section: Many algorithms
Content type: Visual lecture
---
Many algorithms Decision Trees Renamed as CART ID3 ID4.5 C5.0 Etc. | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Decision Trees Revised.pptx
Section: Many algorithms
Content type: Visual lecture
---
Many algorithms Decision Trees Renamed as CART ID3 ID4.5 C5.0 Etc. | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
]
} |
ct_019 | HandsOn1_Window_Functions.md | File: HandsOn1_Window_Functions.md
Section: Hands-On 1 - Window Functions
Content type: Practical lab
---
Create 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: HandsOn1_Window_Functions.md
Section: Hands-On 1 - Window Functions
Content type: Practical lab
---
Create Database/Tables ********************************/ -- Create the windowdb database if it doesn't ... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"Window Functions\"}"
],
"answer_start": [
0
]
} |
ct_020 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Summary
Content type: Visual lecture
---
Pandas A Python library for working with structured data. Series One-dimensional Single column Values + Index DataFrame Two-dimensional Multiple columns Collection of Series Summary | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Summary
Content type: Visual lecture
---
Pandas A Python library for working with structured data. Series One-dimensional Single column Values + Index DataFrame Two-... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_021 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Every Series Has an Index and Values
Content type: Visual lecture
---
Every 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Every Series Has an Index and Values
Content type: Visual lecture
---
Every Series Has an Index and Values Pandas Series: What ages.index # RangeIndex(start=0, stop=... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_022 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: The Index Is Also an Object
Content type: Visual lecture
---
The 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: The Index Is Also an Object
Content type: Visual lecture
---
The Index Is Also an Object Pandas: Index Pandas stores row labels in a separate Index object. obj = pd.... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"
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"answer_start": [
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} |
ct_023 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: When Should You Use a Series?
Content type: Visual lecture
---
When 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: When Should You Use a Series?
Content type: Visual lecture
---
When Should You Use a Series? Pandas Series: When Use a Series when you are focusing on one column, fe... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_024 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Index Labels Are Immutable
Content type: Visual lecture
---
Index 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Index Labels Are Immutable
Content type: Visual lecture
---
Index Labels Are Immutable Pandas: Index You can replace the whole index, but you cannot edit one label i... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_025 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: DataFrame
Content type: Visual lecture
---
A 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: DataFrame
Content type: Visual lecture
---
A dataframe is: Think Of It As Excel Spreadsheet SQL Table Student Records DataFrame Pandas DataFrame: What | Name | Age |... | {
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"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
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"answer_start": [
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ct_026 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Two core structures
Content type: Visual lecture
---
Pandas 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Two core structures
Content type: Visual lecture
---
Pandas provides two fundamental structures: Two core structures Data Structure DataFrame ID Name Age City S1 Abd... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_027 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: What Is an Index?
Content type: Visual lecture
---
What 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 | | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: What Is an Index?
Content type: Visual lecture
---
What Is an Index? Pandas: Index An index identifies rows or values. Pandas creates one automatically if we do not ... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_028 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Default Index vs Custom Index
Content type: Visual lecture
---
Default 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Default Index vs Custom Index
Content type: Visual lecture
---
Default Index vs Custom Index Pandas: Index Custom labels can make row access easier to understand. df... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_029 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Why not just use Python lists?
Content type: Visual lecture
---
Imagine 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Why not just use Python lists?
Content type: Visual lecture
---
Imagine you are managing data for 10,000 students. You now have: 10,000 names 10,000 ages 10,000 citi... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
0
]
} |
ct_030 | Introduction to Pandas_.pptx | File: Introduction to Pandas_.pptx
Section: Simple DataFrame Statistics
Content type: Visual lecture
---
Simple 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Introduction to Pandas_.pptx
Section: Simple DataFrame Statistics
Content type: Visual lecture
---
Simple DataFrame Statistics Pandas DataFrame: Example Pandas can answer table questions with short comma... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_031 | Lecture - Decision Trees.ipynb | File: Lecture - Decision Trees.ipynb
Section: 2. EXERCISE
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture - Decision Trees.ipynb
Section: 2. EXERCISE
Content type: Code notebook
---
[markdown] ## 2. EXERCISE 1. **Tame a tree.** Grow a single `DecisionTreeRegressor` on Ames with `max_depth=None`, then... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"Decision Tree Modeling\"}"
],
"answer_start": [
0
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} |
ct_032 | Lecture - Pandas Basics.ipynb | File: Lecture - Pandas Basics.ipynb
Section: 4. Filtering with conditions
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture - Pandas Basics.ipynb
Section: 4. Filtering with conditions
Content type: Code notebook
---
[markdown] ## 4. Filtering with conditions A comparison on a column returns a boolean Series. Passing i... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
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} |
ct_033 | Lecture - Pandas Basics.ipynb | File: Lecture - Pandas Basics.ipynb
Section: 3. Selecting columns and rows
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture - Pandas Basics.ipynb
Section: 3. Selecting columns and rows
Content type: Code notebook
---
[markdown] ## 3. Selecting columns and rows Two accessors do most selection: - `.loc[rows, cols]` sele... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
0
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} |
ct_034 | Lecture - Pandas Basics.ipynb | File: Lecture - Pandas Basics.ipynb
Section: 1. Series and DataFrame
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture - Pandas Basics.ipynb
Section: 1. Series and DataFrame
Content type: Code notebook
---
[markdown] ## 1. Series and DataFrame A **Series** is a single column: values plus an index (the row labels)... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
0
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} |
ct_035 | Lecture - Pandas Basics.ipynb | File: Lecture - Pandas Basics.ipynb
Section: 2. First look at any dataset
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture - Pandas Basics.ipynb
Section: 2. First look at any dataset
Content type: Code notebook
---
[markdown] ## 2. First look at any dataset Before analysis, always inspect. These four calls answer "wh... | {
"text": [
"{\"Field\": \"Data Science\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Pandas & Data Wrangling\"}"
],
"answer_start": [
0
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} |
ct_036 | Lecture_ML_Workflow.ipynb | File: Lecture_ML_Workflow.ipynb
Section: Recap
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture_ML_Workflow.ipynb
Section: Recap
Content type: Code notebook
---
[markdown] ## Recap - The ML workflow is a fixed loop: frame, prepare, split, choose, fit, predict, evaluate, iterate. It applies ... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_037 | Lecture_ML_Workflow.ipynb | File: Lecture_ML_Workflow.ipynb
Section: 4. Model: Linear Regression
Content type: Code notebook
---
[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 " | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture_ML_Workflow.ipynb
Section: 4. Model: Linear Regression
Content type: Code notebook
---
[markdown] ## 4. Model: Linear Regression **Linear Regression** learns a weighted combination of the feature... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_038 | Lecture_ML_Workflow.ipynb | File: Lecture_ML_Workflow.ipynb
Section: Note
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture_ML_Workflow.ipynb
Section: Note
Content type: Code notebook
---
[markdown] ## Note If you feel like some ML terms such as confusion matrix, precision, recall, train data, test data, etc. are diff... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_039 | Lecture_ML_Workflow.ipynb | File: Lecture_ML_Workflow.ipynb
Section: 7. Evaluate: measure performance on data the model never saw
Content type: Code notebook
---
[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 ... | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture_ML_Workflow.ipynb
Section: 7. Evaluate: measure performance on data the model never saw
Content type: Code notebook
---
[markdown] ## 7. Evaluate: measure performance on data the model never saw ... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_040 | Lecture_ML_Workflow.ipynb | File: Lecture_ML_Workflow.ipynb
Section: 5. Look at the errors, not just the summary numbers
Content type: Code notebook
---
[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 consistent... | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Lecture_ML_Workflow.ipynb
Section: 5. Look at the errors, not just the summary numbers
Content type: Code notebook
---
[markdown] ## 5. Look at the errors, not just the summary numbers A single error num... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_041 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: 3. Explore Data
Content type: Visual lecture
---
X (Feature) Y (Label) Data Exploration 3. Explore Data Understanding the ML Workflow | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: 3. Explore Data
Content type: Visual lecture
---
X (Feature) Y (Label) Data Exploration 3. Explore Data Understanding the ML Workflow | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_042 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Slide 23
Content type: Visual lecture
---
KNN 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Slide 23
Content type: Visual lecture
---
KNN Model X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 6 - generate prediction predicti... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_043 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Interpretation - Feature Importance
Content type: Visual lecture
---
Interpretation - Feature Importance Understanding the ML Workflow Data Collection Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Validation | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Interpretation - Feature Importance
Content type: Visual lecture
---
Interpretation - Feature Importance Understanding the ML Workflow Data Collection Interpretatio... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_044 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Modeling: Decision Tree
Content type: Visual lecture
---
Modeling: Decision Tree Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Modeling: Decision Tree
Content type: Visual lecture
---
Modeling: Decision Tree Understanding the ML Workflow Data Collection Modeling Validation Interpretation De... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_045 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Let’s go through the workflow again, with code demo
Content type: Visual lecture
---
Let’s go through the workflow again, with code demo Understanding the ML Workflow Machine Learning Process - Beginner | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Let’s go through the workflow again, with code demo
Content type: Visual lecture
---
Let’s go through the workflow again, with code demo Understanding the ML Workfl... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_046 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Model Validation
Content type: Visual lecture
---
Source: Overfitting and methods of addressing it (link) Data Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understandi | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Model Validation
Content type: Visual lecture
---
Source: Overfitting and methods of addressing it (link) Data Collection Validation Interpretation Deployment Data ... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_047 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Feature Engineering
Content type: Visual lecture
---
Plant 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Feature Engineering
Content type: Visual lecture
---
Plant Harvest Process Grading Roast Grind Brew Latte https://bradleyboehmke.github.io/HOML/engineering.html Fea... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
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} |
ct_048 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: 4. Split Data into Train/Test
Content type: Visual lecture
---
X (Feature) Y Train Test (Label) Data Exploration 4. Split Data into Train/Test Understanding the ML Workflow | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: 4. Split Data into Train/Test
Content type: Visual lecture
---
X (Feature) Y Train Test (Label) Data Exploration 4. Split Data into Train/Test Understanding the ML ... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_049 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Slide 25
Content type: Visual lecture
---
KNN 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Slide 25
Content type: Visual lecture
---
KNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data # step 8 - load new data i... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_050 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: 2. Label Data
Content type: Visual lecture
---
X (Feature) Y (Label) 2. Label Data Understanding the ML Workflow | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: 2. Label Data
Content type: Visual lecture
---
X (Feature) Y (Label) 2. Label Data Understanding the ML Workflow | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
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} |
ct_051 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Modeling
Content type: Visual lecture
---
Plant 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Modeling
Content type: Visual lecture
---
Plant Harvest Process Grading Roast Grind Brew Latte Modeling Understanding the ML Workflow Data Collection Modeling Valid... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_052 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Slide 19
Content type: Visual lecture
---
X (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:' | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Slide 19
Content type: Visual lecture
---
X (Feature) Y (Label) # step 2 - label data X = iris.loc[:, iris.columns.drop('Species')] y = pd.Series(iris.Species).map(... | {
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"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_053 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Slide 24
Content type: Visual lecture
---
KNN 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Slide 24
Content type: Visual lecture
---
KNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict # step 7 - model evaluation accurac... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_054 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Modeling: Linear Regression
Content type: Visual lecture
---
Modeling: Linear Regression Understanding the ML Workflow Data Collection Modeling Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Modeling: Linear Regression
Content type: Visual lecture
---
Modeling: Linear Regression Understanding the ML Workflow Data Collection Modeling Validation Interpret... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_055 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: 9. Prepare the New Data
Content type: Visual lecture
---
KNN 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: 9. Prepare the New Data
Content type: Visual lecture
---
KNN Model 85% X (Feature) Y Train Test (Label) Data Exploration Model Training Predict New Data Prepared 9.... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_056 | ML Workflow Introduction.pptx | File: ML Workflow Introduction.pptx
Section: Model Validation
Content type: Visual lecture
---
Data Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modeling Model Validation Understanding the ML Workflow Learning Curves | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: ML Workflow Introduction.pptx
Section: Model Validation
Content type: Visual lecture
---
Data Collection Validation Interpretation Deployment Data Preparation Data Exploration Feature Engineering Modelin... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
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} |
ct_057 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 13. Further reading
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 13. Further reading
Content type: Code notebook
---
[markdown] ## 13. Further reading - [Anthropic's prompt engineering guide](https://docs.claude.com/en/docs/build-with... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_058 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 4. Zero-shot
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 4. Zero-shot
Content type: Code notebook
---
[markdown] ## 4. Zero-shot **Zero-shot** means asking for the task with no worked examples. Just the instruction. [code] def... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_059 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 5. Few-shot
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 5. Few-shot
Content type: Code notebook
---
[markdown] ## 5. Few-shot **Few-shot** prompting puts worked examples in the prompt. The model infers the pattern from them, ... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_060 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 1. Setup
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 1. Setup
Content type: Code notebook
---
[markdown] ## 1. Setup [code] !pip install -q transformers [code] import time import json as jsonlib from pathlib import Path im... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
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} |
ct_061 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 12. Where this sits in practice
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 12. Where this sits in practice
Content type: Code notebook
---
[markdown] ## 12. Where this sits in practice **Prompts are hyperparameters, and the harness matters more... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_062 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 2. Anatomy of a prompt
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 2. Anatomy of a prompt
Content type: Code notebook
---
[markdown] ## 2. Anatomy of a prompt The single string you type into a chat box is not what the model receives. In... | {
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"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
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} |
ct_063 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 6. Chain-of-thought
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 6. Chain-of-thought
Content type: Code notebook
---
[markdown] ## 6. Chain-of-thought **Chain-of-thought** prompting asks the model to reason step by step before answeri... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Intermediate\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_064 | Prompt_Engineering.ipynb | File: Prompt_Engineering.ipynb
Section: 3. The evaluation harness, before anything else
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Prompt_Engineering.ipynb
Section: 3. The evaluation harness, before anything else
Content type: Code notebook
---
[markdown] ## 3. The evaluation harness, before anything else This section comes first on... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Prompt Engineering\"}"
],
"answer_start": [
0
]
} |
ct_065 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: 10. Where this sits in practice
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: 10. Where this sits in practice
Content type: Code notebook
---
[markdown] ## 10. Where this sits in practice **RAG is the default way to put an LLM on your ... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_066 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: Failure 2: the context was retrieved but ignored
Content type: Code notebook
---
[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 contrad... | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: Failure 2: the context was retrieved but ignored
Content type: Code notebook
---
[markdown] ### Failure 2: the context was retrieved but ignored The opposite... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_067 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: 9. Exercise
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: 9. Exercise
Content type: Code notebook
---
[markdown] ## 9. Exercise Roughly 15 minutes. The Introduction to Generative AI notebook's exercise built **hybri... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_068 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: 4. Index and retrieve
Content type: Code notebook
---
[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 = | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: 4. Index and retrieve
Content type: Code notebook
---
[markdown] ## 4. Index and retrieve Embedding the corpus is a one-time cost. Queries are then a matrix ... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Intermediate\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_069 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: 1. Setup
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: 1. Setup
Content type: Code notebook
---
[markdown] ## 1. Setup [code] !pip install -q transformers sentence-transformers [code] import time import textwrap ... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Kinesthetic\", \"Level\": \"Beginner\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_070 | Retrieval_Augmented_Generation.ipynb | File: Retrieval_Augmented_Generation.ipynb
Section: 8. Evaluating a RAG system
Content type: Code notebook
---
[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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: Retrieval_Augmented_Generation.ipynb
Section: 8. Evaluating a RAG system
Content type: Code notebook
---
[markdown] ## 8. Evaluating a RAG system The essential discipline: **evaluate retrieval and genera... | {
"text": [
"{\"Field\": \"Generative AI\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"RAG Systems\"}"
],
"answer_start": [
0
]
} |
ct_071 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: RIGHT JOIN
Content type: Visual lecture
---
RIGHT 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: RIGHT JOIN
Content type: Visual lecture
---
RIGHT JOIN 3 OF 4 · EVERY ROW ON THE RIGHT orders customers Shaded = rows returned The mirror of LEFT JOIN. Keep... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_072 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: INSERT
Content type: Visual lecture
---
INSERT 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') | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: INSERT
Content type: Visual lecture
---
INSERT DML: ADD A ROW INSERT INTO table_name (col1, col2, ...) VALUES (val1, val2, ...); -- Example INSERT INTO cust... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_073 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 3 Joins & Unions
Content type: Visual lecture
---
MODULE 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. | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 3 Joins & Unions
Content type: Visual lecture
---
MODULE 3 Joins & Unions The training set doesn't live in one table. Joins are how a behavior log, a... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_074 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: DELETE
Content type: Visual lecture
---
DELETE 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: DELETE
Content type: Visual lecture
---
DELETE DML: REMOVE ROWS DELETE FROM table_name WHERE condition; -- Example DELETE FROM orders WHERE OrderDate < '200... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_075 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 2
Content type: Visual lecture
---
MODULE 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 2
Content type: Visual lecture
---
MODULE 2 Aggregations Aggregate to the product level: a feature table ready to feed a pricing or margin model. SEL... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_076 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: The Superstore Schema
Content type: Visual lecture
---
The 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: The Superstore Schema
Content type: Visual lecture
---
The Superstore Schema TODAY'S DATASET Four tables. Think of orders as your user behavior log, and pro... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_077 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: SELECT
Content type: Visual lecture
---
SELECT 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: SELECT
Content type: Visual lecture
---
SELECT DML: RETRIEVE DATA SELECT column1, column2, ... FROM table_name WHERE condition; -- Example SELECT CustomerNa... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_078 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 2 Aggregations
Content type: Visual lecture
---
MODULE 2 Aggregations GROUP BY and HAVING turn thousands of transaction rows into the customer- and product-level features a model actually trains on. | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: MODULE 2 Aggregations
Content type: Visual lecture
---
MODULE 2 Aggregations GROUP BY and HAVING turn thousands of transaction rows into the customer- and p... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Intermediate\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_079 | SQL_Foundations_for_Data_Science.pptx | File: SQL_Foundations_for_Data_Science.pptx
Section: UPDATE
Content type: Visual lecture
---
UPDATE 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: SQL_Foundations_for_Data_Science.pptx
Section: UPDATE
Content type: Visual lecture
---
UPDATE DML: CHANGE EXISTING ROWS UPDATE table_name SET column1 = value1, column2 = value2 WHERE condition; -- Exampl... | {
"text": [
"{\"Field\": \"SQL\", \"Learning_type\": \"Visual\", \"Level\": \"Beginner\", \"Competency\": \"SQL Foundations\"}"
],
"answer_start": [
0
]
} |
ct_080 | WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx | File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: What is the key difference between traditional programming and machine learning? Correct answer: 2 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: What is the key difference between traditional programming and machine learning? Correct answer: 2 | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_081 | WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx | File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: Scenario: A telecom company wants to flag which customers are likely to cancel their subscription (yes/... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_082 | WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx | File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: Scenario: A retail chain wants to predict how many units of a product it will sell next month, a specif... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_083 | WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx | File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK2_D1_Quiz1_IntroToML_LectureNotebook.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: Scenario: A new team has almost no historical data and no clear, repeatable pattern for the outcome the... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Beginner\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_084 | WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx | File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: A team builds a stacked ensemble combining a random forest, a boosted model, and a linear mod... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
ct_085 | WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx | File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: 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 | Tag this learning-content chunk with Field, Learning_type, Level and Competency. Return ONLY the JSON object.
File: WK4_D2_Quiz2_EnsembleLearningScenarios_Afternoon.xlsx
Section: Create a Quiz
Content type: Quiz
---
Question: A data scientist trains a gradient boosting regressor and steadily increases the number of tr... | {
"text": [
"{\"Field\": \"Machine Learning\", \"Learning_type\": \"Read/Write\", \"Level\": \"Advanced\", \"Competency\": \"Machine Learning Workflow\"}"
],
"answer_start": [
0
]
} |
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