data-leakage
4 free lessons tagged data-leakage 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.
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.
Why Reported Order Book Results Do Not Replicate
At a one-event horizon, 92 percent of mid-price labels are exactly no-change, so a model that always predicts flat scores 92 percent accuracy. This lesson computes that baseline across horizons and works through the four mechanisms that turn a genuine measurement into a number nobody can reproduce.
Validation: Measuring Anything at All
Shuffled cross-validation reports 54 percent accuracy on data containing no signal, because neighbouring samples share their futures. This lesson builds purging, embargo and sample uniqueness weights, shows the illusion scaling with label overlap and vanishing when the fix is applied, and covers why a single walk-forward path is weak evidence.
Why Standard Machine Learning Fails on Market Data
Machine learning practice assumes independent samples, a stable data-generating process, and an environment that does not react to you. Financial data violates all three, and the result is models that validate beautifully and lose money. This lesson establishes what is actually broken before the rest of the course fixes it.