unsupervised

## What I do - Discover hidden patterns in data - Group similar observations without labels - Reduce feature dimensions - Find outliers and anomalies - Learn latent representations - Generate synthetic data ## When to use me Use me when: - Labels are unavailable or expensive - Exploring unknown data patterns - Reducing data complexity - Preprocessing for supervised learning - Real-time clustering needed ## Key Concepts ### Clustering - **K-Means**: Centroid-based, assumes spherical clusters - **Hierarchical**: Dendrogram-based, any distance - **DBSCAN**: Density-based, finds arbitrary shapes - **GMM**: Probabilistic, soft clustering ### Dimensionality Reduction - **PCA**: Linear, variance-based - **t-SNE**: Non-linear, visualization - **UMAP**: Non-linear, structure preservation - **Autoencoders**: Non-linear, learned ### Evaluation - Silhouette score - Davies-Bouldin index - Calinski-Harabasz index - Reconstruction error (for autoencoders)