Скачать или смотреть Machine Learning 08 | Clustering Part 1 | DA | GATE Crash Course

Clustering Part 1 of your GATE Crash Course (Data Analytics/DA & CSE)! We kick off our discussion on Unsupervised Learning by diving deep into the most famous algorithm: K-Means Clustering. Learn the step-by-step iterative process of K-Means—from initial random centroid selection and Euclidean distance calculation to the assignment of data points and centroid re-computation. We will master the core mathematical objective: minimizing the Within-Cluster Sum of Squares (WCSS). Crucially, you will learn the Elbow Method, the industry standard technique for determining the optimal number of clusters (K).

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