Section 5 chapters · 14 lessons
Machine Learning
End-to-end ML workflow with scikit-learn, XGBoost, LightGBM, and the honest engineering practices that prevent silent failures in production.
0 / 14 lessons
- 01
Foundations
3 lessons - 01 What ML actually is in 2026
- 02 The scikit-learn API
- 03 Train/val/test & CV
- 02
Regression
2 lessons - 04 Linear regression
- 05 L1, L2, Elastic Net
- 03
Classification
2 lessons - 06 Logistic regression
- 07 Class imbalance
- 04
Trees & Boosting
4 lessons - 08 Decision trees
- 09 Random forests
- 10 XGBoost, LightGBM, CatBoost
- 11 SHAP & feature importance
- 05
Evaluation
3 lessons - 12 Metrics that matter
- 13 Data leakage
- 14 Hyperparameter tuning