In this video on K-Means Clustering, you'll explore one of the most effective unsupervised learning techniques that groups data points based on their features. It covers the fundamentals of clustering, its real-world applications like customer segmentation and anomaly detection, and provides a detailed walkthrough of the K-Means algorithm, explaining key concepts like centroids and distances. Additionally, practical examples and step-by-step Python implementations using Scikit-learn are included, making it a valuable resource for both beginners and those looking to deepen their understanding of clustering techniques.

K-Means clustering is a popular unsupervised learning algorithm used to partition data into K distinct clusters based on feature similarity. It works by initializing K centroids and assigning each data point to the nearest centroid, forming clusters. The algorithm iteratively updates the centroids by calculating the mean of the points in each cluster, reassigning points to the nearest centroid, and repeating this process until convergence. The goal is to minimize the within-cluster variance, leading to compact and well-separated clusters. K-Means is efficient but sensitive to the initial choice of centroids and requires the number of clusters (K) to be specified beforehand.