Clustering
k-means partitions points around centroids and needs k up front; DBSCAN finds density-connected clusters and labels outliers; hierarchical clustering yields a dendrogram you can cut at any granularity.
Dimensionality reduction
PCA projects data onto the directions of greatest variance, compressing features while retaining structure. UMAP and t-SNE are non-linear methods used for visualising high-dimensional data — useful for inspection, not as model inputs.
Embeddings
An embedding maps a discrete item — a word, a user, a product — to a dense vector where distance means similarity. Embeddings are the shared currency of modern AI: search, recommendation and retrieval-augmented generation all run on nearest-neighbour lookups in embedding space.