Machine learning, explained
From a 1950s checkers program to models running in production: learning from data, splitting it, scoring it, and keeping models honest once they ship.
8 lessons · 53 min
- 1What machine learning isFreeArthur Samuel's checkers program improved by playing, and in 1959 his paper named the field. What learning from data means, and when rules are better.1956–1963 · 6 min
- 2Supervised learning: features and labelsIn 2002 Paul Graham let 8,000 labelled emails write his spam filter. How supervised learning maps features to labels, and regression versus classification.2002 · 6 min
- 3Unsupervised learning: clusters and anomaliesA Bell Labs draft from 1957 gave us k-means. How models find groups in unlabelled data, and how the same idea spots the transaction that does not fit.1957–1982 · 6 min
- 4Training, validation and test setsThe Netflix Prize hid its deciding data from every leaderboard. Why models are judged on data they never saw, and how to spot overfitting and underfitting.2006–2009 · 7 min
- 5Evaluating models: beyond accuracyA Cranfield librarian gave us precision and recall. The confusion matrix, a worked example in numbers, and why 95% accuracy can mean a useless model.1957–1972 · 7 min
- 6Decision trees, random forests and boostingLeo Breiman judged models by how well they predict. One tree, a forest of them, and boosted trees that fix each other's mistakes, without the maths.1984–2016 · 7 min
- 7Data quality, bias and leakageAmazon's hiring model learned to mark down women because its data told it to. How flawed data, inherited bias and leaked answers make models wrong.1990s–2018 · 7 min
- 8Models in production: MLOps, drift and retrainingGoogle Flu Trends worked, then quietly drifted until it doubled the real figures. Deploying models, watching them for drift and retraining them safely.2008–2015 · 7 min