quiz links, quizzes 7 and 8, Regression Lesson 1
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Jen Looper
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@ -7,7 +7,7 @@ Add a sketchnote if possible/appropriate
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[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
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> Video: Microsoft's Approach to Responsible AI
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## [Pre-lecture quiz](link-to-quiz-app)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
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Describe what we will learn
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@ -54,7 +54,7 @@ Add a challenge for students to work on collaboratively in class to enhance the
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Optional: add a screenshot of the completed lesson's UI if appropriate
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## [Post-lecture quiz](link-to-quiz-app)
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
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## Review & Self Study
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@ -3,7 +3,7 @@
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> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
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## Introduction
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The lessons in this section cover types of Regression in the context of machine learning. Regression models can help determine the relationship between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes datapoints.
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@ -177,7 +177,7 @@ Congratulations, you just built your first Linear Regression model, created a pr
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## 🚀Challenge
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Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
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## Review & Self Study
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@ -3,7 +3,7 @@
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> 
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> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
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### Introduction
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@ -143,7 +143,7 @@ This is a more useful data visualization! It seems to indicate that the highest
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## 🚀Challenge
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Explore the different types of visualization that matplotlib offers. Which types are most appropriate for regression problems?
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
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## Review & Self Study
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@ -2,7 +2,7 @@
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> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
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### Introduction
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So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this unit. You have also visualized it using Matplotlib. Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: basic linear regression and polynomial regression, along with some of the math underlying these techniques.
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@ -249,7 +249,7 @@ It does make sense! And, if this is a better model than the previous one, lookin
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Test several different variables in this notebook to see how correlation corresponds to model accuracy.
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
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## Review & Self Study
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@ -1,5 +1,5 @@
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# Logistic Regression to Predict Categories
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
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## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
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### Introduction
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@ -218,7 +218,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
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## 🚀Challenge
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There's a lot more to unpack regarding Logistic Regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets.
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
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## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
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## Review & Self Study
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@ -109,6 +109,332 @@
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]
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}
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]
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},
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{
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"id": 3,
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"title": "Lesson 1 - Intro to ML: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "q1",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q2",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "true"
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},
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{
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"answerText": "b",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q3",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 4,
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"title": "Lesson 1 - Intro to ML: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "q1",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q2",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "true"
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},
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{
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"answerText": "b",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q3",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 5,
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"title": "Lesson 1 - Intro to ML: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "q1",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q2",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "true"
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},
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{
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"answerText": "b",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q3",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 6,
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"title": "Lesson 1 - Intro to ML: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "q1",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q2",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "true"
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},
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{
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"answerText": "b",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "q3",
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"answerOptions": [
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{
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"answerText": "a",
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"isCorrect": "false"
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},
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{
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"answerText": "b",
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"isCorrect": "true"
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},
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{
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"answerText": "c",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 7,
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"title": "Lesson 7 - Get started with Python and Scikit-Learn for Regression models: Pre-Lecture Quiz",
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"quiz": [
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{
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"questionText": "Which of these variables is a numeric variable?",
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"answerOptions": [
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{
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"answerText": "Height",
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"isCorrect": "true"
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},
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{
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"answerText": "Gender",
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"isCorrect": "false"
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},
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{
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"answerText": "Hair Color",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "Which of these variables is a categorical variable?",
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"answerOptions": [
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{
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"answerText": "Heart Rate",
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"isCorrect": "false"
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},
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{
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"answerText": "Blood Type",
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"isCorrect": "true"
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},
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{
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"answerText": "Weight",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "Which of these problems is a Regression analysis-based problem?",
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"answerOptions": [
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{
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"answerText": "Predicting the final exam marks of a student",
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"isCorrect": "true"
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},
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{
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"answerText": "Predicting the blood type of a person",
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"isCorrect": "false"
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},
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{
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"answerText": "Predicting whether an email is spam or not",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 8,
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"title": "Lesson 8 - Get started with Python and Scikit-Learn for Regression models: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?",
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"answerOptions": [
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{
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"answerText": "Overfitting",
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"isCorrect": "true"
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},
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{
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"answerText": "Underfitting",
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"isCorrect": "false"
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},
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{
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"answerText": "Double Fitting",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "The process of identifying significant features from a set of features is called:",
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"answerOptions": [
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{
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"answerText": "Feature Extraction",
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"isCorrect": "false"
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},
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{
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"answerText": "Feature Dimensionality Reduction",
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"isCorrect": "false"
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},
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{
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"answerText": "Feature Selection",
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"isCorrect": "true"
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}
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]
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},
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{
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"questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:",
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"answerOptions": [
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{
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"answerText": "Cross-Validation",
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"isCorrect": "false"
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},
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{
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"answerText": "Hold-Out Validation",
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"isCorrect": "true"
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},
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{
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"answerText": "Leave one out Validation",
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"isCorrect": "false"
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}
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]
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}
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]
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}
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]
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}
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