EM Algorithm In Machine Learning | Expectation-Maximization | Machine Learning Tutorial | Edureka
Taha GÖÇER
About this course
This course provides a comprehensive overview of the Expectation-Maximization (EM) algorithm in machine learning, focusing on its application in handling latent variables, Gaussian mixture models, and maximum likelihood estimation. Learners will delve into the workings of the EM algorithm, visualizing data with Jupyter Notebook, and explore its advantages and disadvantages in practical scenarios.
What you should already know
Learners should have foundational knowledge of machine learning concepts, statistical modeling, and proficiency in Python programming.
What you will learn
By the end of this course, learners will be able to effectively implement the EM algorithm, understand its application in Gaussian mixture models, and recognize its advantages and limitations in various use cases.