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Free public beta until December 4, 2026: every course is open, no card needed. After that, the first chapter of each course stays free and full access is a one-time $39 ($19 for up to 100 founding members chosen for helpful feedback). Your progress is kept. How the beta works

Problem-solving recipes

A step-by-step procedure for every problem type this course teaches — what to do first, what to do next, and how to tell which case you're in.

Decision trees

Grow and prune a decision tree

Use when: you have training data and want a tree that works well on new data, not just one that fits the training data closely

Goal: a pruned tree, with alpha chosen by cross-validation

Cost-complexity pruning

Unsupervised learning: clustering and dimensionality reduction

Run PCA in practice

Use when: you have a design matrix with many features and want to reduce it to a smaller number of useful components

Goal: a reduced design matrix, with a clear reason for how many components you kept

Running PCA in practice

Practicing responsibly: failure modes, ethics, and deployment

Diagnose a failing model

Use when: a trained model underperforms and you need to find the actual root cause, not just retune blindly

Goal: the correct root cause, found in the order it is most likely to be found, without wasted effort chasing the wrong fix

Why models fail: a troubleshooting order

Capstone: thinking like a practitioner

The general classification recipe

Use when: you have a labeled dataset and need to predict a discrete category

Goal: a tuned, honestly evaluated classification model with a documented baseline comparison

Which algorithm should I use?

The general regression recipe

Use when: you have a labeled dataset and need to predict a continuous numeric value

Goal: a tuned, honestly evaluated regression model with a documented baseline comparison

Which algorithm should I use?