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| 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 |
| 01 | [Introduction](Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](Introduction/1-intro-to-ML/README.md) | Team |
| 02 | [Introduction](Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | [Introduction](Introduction/README.md) | Fairness and Machine Learning | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](Introduction/3-fairness/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 |
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| 09 | Introduction to Classification | [Classification](Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](Classification/1-Data/README.md) | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Discriminative Model | [lesson](Classification/2-Descriminative/README.md) | Cassie |
| 11 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Generative Model | [lesson](Classification/3-Generative/README.md) | Cassie |
| 12 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Cassie |
| 12 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Jen |
| 13 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | Jen |
| 14 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](Clustering/README.md) | Explore the K-Means Clustering Method | [lesson](Clustering/2-K-Means/README.md) | Jen |
| 15 | Introduction to Natural Language Processing ☕️ | [Natural Language Processing](NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](NLP/1-Introduction-to-NLP/README.md) | Stephen |
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| 21 | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ | [Time Series](TimeSeries/README.md) | Time Series Forecasting with ARIMA | [lesson](TimeSeries/2-ARIMA/README.md) | 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) | Ornella |
| 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) | Team |
## 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`. The website will be served on port 3000 on your localhost: `localhost:3000`.

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