Machine Learning Foundations: Probability
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Description
This course offers an in-depth exploration of probability and its application in the design and implementation of reliable machine learning algorithms. It covers the core concepts and functionalities of probability, including the rules of probability, joint and marginal probability, discrete and continuous probability distributions, and Bayes' theorem. By the end of the course, you'll have the essential tools and techniques for successful probabilistic modeling in machine learning.
Learning Objectives
- Learning basics of probability theory in machine learning.
- Understanding sum rule, product rule, and conditional probability.
- Calculating joint and marginal probability.
- Exploring common discrete probability distributions.
- Learning about continuous probability distribution.
- Discovering Bayes' theorem and its applications.