Unsupervised Learning — Machine Learning Roadmap
Finding structure in data that has no labeled outcome
Steps in Unsupervised Learning
- k-Means Clustering — intermediate · Partitioning data into k groups based on similarity
- Hierarchical Clustering — intermediate · Building a tree of nested clusters without pre-specifying the number of clusters
- DBSCAN — intermediate · Density-based clustering that can find arbitrarily shaped clusters and outliers
- Dimensionality Reduction with PCA — intermediate · Reducing the number of features while preserving as much variance as possible
- t-SNE & UMAP for Visualization — advanced · Visualizing high-dimensional data in two or three dimensions
- Anomaly Detection — advanced · Identifying rare or unusual data points using unsupervised techniques
Part of
- Machine Learning roadmap — the full learning path