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Decision Trees

Decision Trees

$39.00Price

You can already ask an AI to write you a Decision Tree. The problem is knowing whether it's actually learning structure in your data — or whether it's just overfitting, splitting on noise until it memorizes the training set and then falls apart on anything it hasn't seen. This is the course that gives you that judgment.


Five parts, one algorithm, no gaps:

  • Part 1 — Intro. How impurity, information gain, and recursive splitting — the moves that turn a table of features into a series of yes/no questions — carve a feature space into regions without ever needing a derivative or a smooth landscape. The intuition before the notation.

  • Part 2 — By Hand. A complete worked example, split by split: measure the impurity at the root, evaluate every candidate split, compute the information gain, choose the winner, recurse. Gini and entropy both shown, every count and every fraction on the page. 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 watch the tree grow node by node — and see for yourself what changing max depth, minimum samples per leaf, or the impurity criterion actually does to the splits.

  • 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 or prediction problem, you'll know how to choose and encode features, tune depth and stopping criteria on purpose, and tell when the tree is overfitting rather than learning.

Written for practicing engineers and STEM professionals who'd rather understand a method than trust a black box.

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