Machine Learning Guide

Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods like DBSCAN or HDBSCAN. For determining the optimal number of clusters, silhouette score is generally preferred over inertia-based visual heuristics, and it natively supports pre-computed distance matrices. Links Notes and resources at ocdevel.com/mlg/mla-11 Try a walking desk stay healthy & sharp while you learn & code K-means Clustering K-means is the most widely used clustering algorithm and is typically the first method to try for general clustering tasks. The scikit-learn KMeans implementation is suitable for small to medium-sized datasets, while Faiss's kmeans is more efficient and accurate for very large datasets. K-means requires the number of clusters to be specified in advance and relies on the Euclidean distance metric, which performs poorly in high-dimensional spaces. When document embedding