Intermediate · every module free · 7 challenges
Linear regression derived, implemented and diagnosed — the template every supervised model follows.
Decision boundaries, cross-entropy, thresholds, and the metrics that matter when classes are imbalanced.
L1, L2, cross-validation, learning curves, and deciding what to do next from evidence rather than instinct.
Decision trees to XGBoost and LightGBM — still the strongest baseline on tabular data, and interviewers know it.
Clustering, dimensionality reduction, density estimation and anomaly detection without labels to check yourself against.
Collaborative filtering, matrix factorization, two-tower retrieval and the cold start problem.
MDPs, value and policy methods, Q-learning and policy gradients — the machinery RLHF is built from.