Naive Bayes
Naive Bayes
From Hand Calculations to AI-Assisted Code
You can already ask an AI to write you a Naive Bayes classifier. The problem is knowing why it's reporting 99.9% confidence on a prediction that's dead wrong — or why one word it never saw in training just zeroed out an entire class. Both are famous Naive Bayes traps, and both are invisible if you don't understand what the model is actually computing. This is the course that gives you that judgment.
Five parts, one algorithm, no gaps:
Part 1 — Intro. How Bayes' theorem turns "how likely is this evidence given each class?" into "which class is this?" — and why the deliberately naive assumption that features are independent makes the whole thing fast and surprisingly effective, even when the assumption isn't strictly true. The intuition before the notation.
Part 2 — By Hand. A complete worked example: compute the priors, build the likelihood table, apply Bayes' rule, and classify a new case — every probability and every number shown. Nothing waved away, nothing left "as an exercise." You prove the algorithm to yourself.
Part 3 — In Excel. The same example built out in a spreadsheet you can open and poke at, so you see exactly how each feature shifts the posterior — and watch the zero-frequency problem appear, then disappear once smoothing is added.
Part 4 — In Python. A clean, executable, verified reference implementation — the known-good version you can check any other code against.
Part 5 — With AI. A tested prompt that directs an AI assistant to carry this same algorithm to your problem, plus the understanding to catch it when it's wrong.
By the end you won't just have working code. You'll have followed the algorithm from first principles through a hand calculation, a spreadsheet, and production Python — so when you point an AI at your own classification problem, you'll know why smoothing matters, how to read the probabilities it produces without over-trusting them, and how to tell when the answer is right.
Written for practicing engineers and STEM professionals who'd rather understand a method than trust a black box.
