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All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 48 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder. -| Lesson Number | Project Name/Group | Concepts Taught | Learning Objectives | Linked Lesson | Author | -| :-----------: | :--------------------------------------: | :------------------------------------------: | --------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :-------: | -| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Amy | -| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Amy | -| 03 | [Introduction](1-Introduction/README.md) | The Ethics of Machine Learning | What are the important ethical issues that students should consider when building and applying ML models? | [lesson](1-Introduction/3-Ethics/README.md) | Tomomi | -| 04 | Introduction to Regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](2-Regression/1-Tools/README.md) | Jen | -| 05 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen | -| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/README.md) | Jen | -| 07 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen | -| 08 | Introduction to Classification | [Classification](3-Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](3-Classification/1-Data/README.md) | Cassie | -| 09 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Discriminative Model | [lesson](3-Classification/2-Descriminative/README.md) | Cassie | -| 10 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Generative Model | [lesson](3-Classification/3-Generative/README.md) | Cassie | -| 11 | Delicious Asian Recipes 🍜 | [Classification](3-Classification/README.md) | Build a Web App using your Model | [lesson](3-Classification/4-Applied/README.md) | Cassie | -| 12 | Introduction to Clustering | [Clustering](4-Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](4-Clustering/1-Visualize/README.md) | | -| 13 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](4-Clustering/README.md) | Explore the K-Means Clustering Method | [lesson](4-Clustering/2-K-Means/README.md) | | -| 14 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](4-Clustering/README.md) | Explore Centroid models for Clustering | [lesson](4-Clustering/3-Centroid/README.md) | | -| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](4-Clustering/README.md) | Build an API for Clustering Recommendation tasks | [lesson](4-Clustering/4-API/README.md) | | -| 16 | Introduction to NLP | [Natural Language Processing]() | tbd | [lesson]() | Stephen | -| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen | -| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen | -| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen | -| 20 | Introduction to Time Series Forecasting | [Time Series]() | tbd | [lesson]() | Francesca | -| 21 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca | -| 22 | Introduction to Reinforcement Learning | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry | -| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry | -| 24 | Real-World ML Scenarios and Applications | ML in the Wild | Interesting and Revealing real-world applications of classical ML | [lesson](8-Real-World/1-Applications/README.md) | All | +| Lesson Number | Section | Concepts Taught | Learning Objectives | Linked Lesson | Author | +| :-----------: | :--------------------------------------: | :-----------------------------------------------: | --------------------------------------------------------------------------------------------------------- | :-------------------------------------------------: | :-------: | +| 01 | [Introduction](Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](Introduction/1-intro-to-ML/README.md) | Amy | +| 02 | [Introduction](Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Amy | +| 03 | [Introduction](Introduction/README.md) | The Ethics of Machine Learning | What are the important ethical issues that students should consider when building and applying ML models? | [lesson](Introduction/3-Ethics/README.md) | Tomomi | +| 04 | Introduction to Regression | [Regression](Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](Regression/1-Tools/README.md) | Jen | +| 05 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](Regression/2-Data/README.md) | Jen | +| 06 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](Regression/3-Linear/README.md) | Jen | +| 07 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build a Logistic Regression model | [lesson](Regression/4-Logistic/README.md) | Jen | +| 08 | Introduction to Classification | [Classification](Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](Classification/1-Data/README.md) | Cassie | +| 09 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Discriminative Model | [lesson](Classification/2-Descriminative/README.md) | Cassie | +| 10 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Generative Model | [lesson](Classification/3-Generative/README.md) | Cassie | +| 11 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Cassie | +| 12 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | | +| 13 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](Clustering/README.md) | Explore the K-Means Clustering Method | [lesson](Clustering/2-K-Means/README.md) | | +| 14 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](Clustering/README.md) | Explore Centroid models for Clustering | [lesson](Clustering/3-Centroid/README.md) | | +| 15 | An API Endpoint 🔌 | [API](API/README.md) | Build an API endpoint to query your trained models | [lesson](API/Api-Endpoint/README.md) | Jen | +| 16 | Introduction to NLP | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen | +| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen | +| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen | +| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](NLP/README.md) | tbd | [lesson]() | Stephen | +| 20 | Introduction to Time Series Forecasting | [Time Series](Time-Series/README.md) | tbd | [lesson]() | Francesca | +| 21 | Power Usage in India ⚡️ | [Time Series](Time-Series/README.md) | tbd | [lesson]() | Francesca | +| 22 | Introduction to Reinforcement Learning | [Reinforcement Learning](Reinforcement/README.md) | tbd | [lesson]() | Dmitry | +| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning](Reinforcement/README.md) | tbd | [lesson]() | Dmitry | +| 24 | Real-World ML Scenarios and Applications | ML in the Wild | Interesting and Revealing real-world applications of classical ML | [lesson](Real-World/1-Applications/README.md) | All | ## Offline access You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. 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