Mastering Clustering in Machine Learning

This intermediate path explores clustering as a core unsupervised machine learning technique. You will group observations based on similarity and interpret the resulting structures.

You will examine K-means, density-based clustering with DBSCAN, and hierarchical clustering using dendrograms. The path explains how each approach identifies clusters and how to select an algorithm for different data characteristics.

You will also evaluate and refine clustering results using the Silhouette Score, Davis-Bouldin Index, and cross-tabulation analysis. This path is designed for learners with foundational machine learning and data analysis experience who want to build practical clustering skills.