K-Means - Explained

K-Means Clustering is one of the most important unsupervised learning algorithms in machine learning and data science. This video explains how k-means works step by step, including centroid initialization, the assignment step, the update step, convergence, objective function minimization, and sensitivity to initialization. Perfect for beginners learning clustering, machine learning algorithms, data analysis, and pattern recognition. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The Hessian Matrix: https://youtu.be/9tp1kULwU2w The Jacobian Matrix: https://youtu.be/6FesMicc844 Bayesian Optimizatio…