Data Science, Unsupervised Learning, Machine Learning, Gaussian Mixture Model, Coding.
by maximizing the expected likelihood found on the E-step. The parameters found on the M-step are then used to begin another E-step, and the process is repeated until convergence. From sklearn, we use the GaussianMixture class which implements the EM algorithm for fitting a mixture of Gaussian