Core Concepts of Unsupervised ML

TOPIC I COVERED :- • Foundations of Unsupervised Machine Learning • Characteristics of Unlabeled Data and Learning Without Targets • Core Tasks in Unsupervised ML (Clustering & Anomaly Detection) • Overview of Clustering Techniques • K-Means Clustering: Concept, Objective Function, and Workflow • Elbow Method for Optimal Cluster Selection (WCSS Analysis) • K-Means Implementation and Visualization • Hierarchical Clustering: Theory, Types, and Dendrogram Analysis • Agglomerative Clustering Algorithm: Step-by-Step Process • Linkage Criteria (Single, Complete, Average, Ward’s) • Comparative Analysis: K-Means vs. Hierarchical Clustering • DBSCAN: Definition, Density-Based Logic, and Core Concepts • DBSCAN Point Categories (Core, Border, Noise) • DBSCAN Step-Wise Working and Reachability Concepts • DBSCAN Implementation with Example Plot • Dimensionality Reduction: Purpose and Approaches • Principal Component Analysis (PCA): Intuition and Key Concepts • PCA Mathematical Workflow (Standardization, Covariance Matrix)