Machine Learning

Learning patterns from data: training, validation, overfitting.

Machine Learning is taught here in 13 lessons, running from Machine learning in one page through to Deploying and monitoring a model. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.

Lessons

1. Machine learning in one pageSupervised, unsupervised and reinforcement learning, what features and labels are, and how to tell whether learning happened at all.
2. Evaluation and overfittingCross-validation, the precision/recall trade-off, and how to spot a model that memorised the training set.
3. Pipelines and saving modelsChaining preprocessing with a model so nothing leaks, and persisting the whole pipeline for consistent inference.
4. Data collection, cleaning and feature engineeringWhere training rows come from, how to treat missing values and outliers without lying to the model, and how to turn raw columns into signals.
5. Splitting data correctlyTrain, validation and test roles, stratified, grouped and time-series splits, leakage through preprocessing, and nested cross-validation.
6. Supervised algorithms in practiceLinear and logistic regression, k-NN, trees, forests, gradient boosting and SVMs, with the assumptions each one quietly makes about your data.
7. Unsupervised learningK-means, hierarchical and density-based clustering, PCA and manifold methods, and anomaly detection when nobody has labelled the odd cases.
8. Classification metrics in depthConfusion matrices, ROC-AUC versus precision-recall curves, choosing an operating threshold, and checking whether probabilities are calibrated.
9. Regression metrics and residual analysisMAE, RMSE, MAPE and their failure modes, quantile loss for asymmetric costs, and how residual plots expose systematic error a single number hides.
10. Imbalanced dataWhy accuracy is the wrong headline, resampling and class weights, synthetic oversampling done safely, threshold tuning, and evaluating with precision-recall.
11. Hyperparameter tuning and experiment trackingGrid, random and Bayesian search, early stopping as a cheap budget saver, logging runs and seeds, and reporting uncertainty on the result.
12. Interpretability, bias and fairnessPermutation importance, partial dependence, SHAP, subgroup metrics, where bias enters, and documenting what a model must not be used for.
13. Deploying and monitoring a modelBatch scoring versus an API, keeping features identical between training and serving, detecting drift, and knowing when to retrain or roll back.

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Last refreshed 2026-09-18.