Soft Clustering with Gaussian Mixture Models (GMM) and EM Algorithm

In this video, we explore Gaussian Mixture Models (GMMs) and how they perform soft clustering, allowing each data point to belong to multiple clusters with different probabilities. We also walk through the Expectation-Maximization (EM) algorithm, including the E-step and M-step, and see how GMM learns cluster parameters iteratively. Topics covered: • Gaussian distributions in clustering • Soft vs. hard clustering • Hidden variables and cluster probabilities • Expectation (E) step • Maximization (M) step • Weighted mean and variance updates • 1-D GMM example Perfect for students taking Data Mining, Machine Learning, AI, or Data Science courses. #MachineLearning #DataMining #GaussianMixtureModel #GMM #EMAlgorithm #Clustering #DataScience #ArtificialIntelligence #Statistics #PythonML