If you work with machine learning models, you probably already know that your models are based on estimation and approximation. Probability is everything and more—but how do you leverage it to your advantage?

In this course, the third part of the Machine Learning Foundations series, join instructor Terezija Semenski for an in-depth exploration of probability, its core concepts and functionalities, and how to use it to design, implement, and manage more reliable machine learning algorithms. Along the way, discover some of the most essential tools and techniques you need to know for successful probabilistic modeling, pulling from the rules of probability, joint and marginal probability, discrete probability distributions, continuous probability distributions, Bayes' theorem, and more.