This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include:
- Refreshers in related topics that highlight the key points of the prerequisites of the course.
- Cheatsheets for each machine learning field, as well as another dedicated to tips and tricks to have in mind when training a model.
- All elements of the above combined in an ultimate compilation of concepts, to have with you at all times!
The material presented in this repo has the following characteristics.
| Category | Topic | Preview |
|---|---|---|
| VIP Cheatsheet | Supervised learning |  |
| VIP Cheatsheet | Unsupervised learning |  |
| VIP Cheatsheet | Deep learning |  |
| VIP Cheatsheet | Tips and tricks |  |
| VIP Refresher | Probabilities and statistics |  |
| VIP Refresher | Linear algebra and calculus |  |
| Super VIP Cheatsheet | Everything related to machine learning |  |
This material is also available on a dedicated website, so that you can enjoy reading it from any device.
Afshine Amidi and Shervine Amidi.
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