| 1. Introduction to Supervised and Unsupervised Learning |
1. Dynamic Programming with Rmarkdown |
| 2. Statistical Learning Theory |
2. Version Control with Git and GitHub |
| 3. Linear Regression |
3. Introduction to the Tidyverse |
| 4. Classification |
4. Machine Learning Workflows with Tidymodels |
| 5. Resampling Methods |
5. Feature Engineering |
| 6. Linear Model Selection and Regularization |
6. Imbalanced Learning |
| 7. Non-Linear Regression Methods |
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| 8. Tree-based Methods |
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| 9. Support Vector Machines |
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| 10. Deep Learning |
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| 11. Unsupervised Learning |
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