ML is fundamentally about uncertainty. Bayes theorem, Gaussian distributions, and maximum likelihood estimation give models a rigorous probabilistic foundation.
Learning Objectives
→Apply Bayes theorem to update beliefs
→Define and maximize log-likelihood
→Understand the Gaussian distribution parameters
→Implement a Naive Bayes classifier
Practice
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