A series of introductory workbooks I have created to teach machine learning to people who want to use it in an applied context.
Exercise solutions are available upon request. The reason is in case I want to use these as exercises for graded homework in the future (similar policy as openintro). I may aim in the future to similarly break these into even and odd questions and only restrict the odd questions...
Plan
| Workbook | Topic | Colab | % Complete |
|---|---|---|---|
| 01 | End-to-End |  |
95% |
| 02 | Data and Features | |
95% |
| 03 | Linear Regression | |
95% |
| 04 | Non-Linear Regression and Regularisation | |
10% |
| 05 | "Classical" Classification | |
50% |
| 06 | Ensemble Classification | |
20% |
| 07 | Dimension Reduction | |
10% |
| 08 | Distance-Based Models | |
50% |
| 09 | Natural Language Processing | |
5% |
| 10 | Time Series | |
95% |
| 11 | Neural Networks pt.1 | 0% | |
| 12 | Neural Networks pt.2 | 0% | |
| 13 | Ethics | 0% |