AI Engineering/Phase 01/Lesson 07

Bayes' Theorem

Probability is about what you expect. Bayes' theorem is about what you learn.

Build 1.3 hr Python

Prerequisites: Phase 1, Lesson 06 (Probability Fundamentals)

#Learning Objectives

Apply Bayes' theorem to compute posterior probabilities from priors, likelihoods, and evidence
Build a Naive Bayes text classifier from scratch with Laplace smoothing and log-space computation
Compare MLE and MAP estimation and explain how MAP corresponds to L2 regularization
Implement sequential Bayesian updating using Beta-Binomial conjugate priors for A/B testing

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.