Unsupervised Learning
A 16-module path through unsupervised learning, in study order from foundations to the frontier.
Modules
- The Unsupervised Learning Problem
- Density Estimation Fundamentals
- Distance Metrics and Similarity Measures
- K-Means Clustering
- K-Means++ Initialization
- Hierarchical Clustering
- DBSCAN and Density-Based Clustering
- Choosing the Number of Clusters: The Elbow Method
- Cluster Validation with Silhouette
- Gaussian Mixture Models
- The EM Algorithm
- Principal Component Analysis
- Singular Value Decomposition
- Manifold Learning and Nonlinear Dimensionality Reduction
- t-SNE and UMAP
- Anomaly Detection Fundamentals