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| WELCOME ▶︎ START HERE |
| Weeks |
- 1: Introductions and Reviews
- 2: First Things First
- 3: Basics of Learning and Probabilistic Learning
- 4: Naïve Bayes and KNN
- 5: Decision Tree and Midterm
- 6: Linear Models and Unsupervised Learning
- 7: Neural Networks and Deep Learning
- 8: Performance, Data, and Dimensionality
- 9: Model Refinement and Ensemble Learning
- 10: Presentations and Review