@ -59,32 +59,32 @@ By ensuring that the content aligns with projects, the process is made more enga
> **A note about quizzes**: 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.
| 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) | |
| 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 |
| 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) | |
| 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`. The website will be served on port 3000 on your localhost: `localhost:3000`.
Jen Looper
3 years ago
