Practical Data Science using Python - K-Means Clustering Computation

The video tutorial explains the K-means clustering algorithm, focusing on determining the number of clusters (K) and optimizing centroids. It covers the process of initializing random centroids, calculating Euclidean distances, and iteratively refining centroids to minimize the sum of squared distances. A detailed example illustrates these steps, emphasizing the importance of centroid optimization in achieving accurate clustering results.

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