The Expectation-Maximization (EM) algorithm is a cornerstone in the field of machine learning, particularly in the realms of statistical estimation and clustering. It can be used as a powerful tool to determine the maximum probability estimates of the parameters that are derived from probabilistic theories, specifically where the model relies on latent variables that are not observed. This EM algorithm is known for its use across a variety of areas like natural computer vision, language processing bioinformatics, bioinformatics, and many more. Its capability to deal with incomplete data sets and its versatility in formulating models make it an essential tool for both researchers and professionals. https://www.sevenmentor.com/data-science-course-in-pune.php Understanding the EM Algorithm The EM algorithm is an iterative process to discover the most likely or maximum A posteriori (MAP) estimations of the parameters in statistical models, in which the model is based on unknown latent variables. The algorithm is based on two steps that are called the expectation step (E-step) as well as the Maximization step (M-step) which is why it has the name. Step of Expectation (E-step): In this step, it is where the algorithm calculates an expected amount of log-likelihood formula about the conditional spread of latent variables, based on the observations of data as well as the current estimate of the parameters of the model. This is filling in the data that is missing by estimating, which makes the next step of maximization computationally feasible. Maximization Step (M-step): Based on the assumptions computed in this step, the M step determines the parameters that will maximize the log-likelihood expected during step E. The M-step updates the models' parameters to improve the probability of results based on these updated parameters. The algorithm alternates between the two steps until it reaches a point of convergence, which means that the change in the log-likelihood, or parameter estimates are below a threshold that is predefined indicating that a local peak in the probability function is identified.