scikit-learn
3 free lessons tagged scikit-learn across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Decision Trees, Random Forests and Gradient Boosting
Tree ensembles remain the default for tabular data. Learn how a decision tree picks its splits, why one deep tree memorises, how random forests average away variance and gradient boosting corrects errors in sequence, and how to read feature importance without being misled.
Generalization: Measuring What a Model Actually Learned
Training error flatters every model. Learn how train, validation and test splits work, watch overfitting happen in computed numbers, and see how regularization, cross-validation and leak-proof pipelines give you a score you can trust on data the model has never seen.
What Machine Learning Does: Learning a Function From Examples
Machine learning replaces hand-written rules with a function fitted to examples. Learn the supervised setup, what a loss function measures, how a linear regression is actually fitted, why a baseline comes first, and when a problem is better solved without machine learning at all.