Core machine learning concepts: clustering, evaluation metrics, and classical algorithms.
How the Calinski-Harabasz index evaluates clustering quality by measuring the ratio of between-cluster to within-cluster variance — fast, intuitive, and ideal for k-selection with convex clusters.
How the silhouette score measures clustering quality for every individual point — comparing intra-cluster cohesion to nearest-cluster separation, with per-point diagnostics that work for arbitrary cluster shapes.
How the Davies-Bouldin index evaluates clustering quality by finding each cluster's most similar neighbor — a pessimistic, worst-case metric that catches overlapping cluster pairs.
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.