Machine Learning
🤖 ML Foundations
The core ideas every machine learning practitioner needs — understanding what makes a model good, bad, or overfit.
3 concepts— start at the top and work your way down
- 1→
Regularization
Adding a penalty on model complexity to prevent overfitting — L1 (Lasso) induces sparsity, L2 (Ridge) shrinks coefficients smoothly.
- 2→
Cross-Validation
Estimating model generalisation by repeatedly training on subsets and evaluating on the held-out remainder — k-fold, leave-one-out.
- 3→
Model Evaluation
Confusion matrices, accuracy, precision, recall, F1 score, ROC curves, and AUC — the toolkit for measuring classifier and regressor performance.