Phase 2: Probability & Statistics · ~75 minutes · Python
Bayes' Theorem
Probability is about what you expect. Bayes' theorem is about what you learn.
What you will learn
- 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
Introduction
- Type: Build
- Language: Python
- Prerequisites: Probability Fundamentals
- Time: ~75 minutes
Objective
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
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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