Machine Learning

What this area actually covers

Machine learning, as a discipline you would be interviewed on, is the work of turning a decision somebody has to make into a prediction problem, learning a function that makes that prediction from historical data, and establishing honestly how well it does — where honestly means "on data it has not seen, measured by something that corresponds to the decision, at the operating point you will actually run".

That definition puts most of the weight before any algorithm is chosen, which is where the weight belongs. The choice between logistic regression and gradient-boosted trees is a real decision with real consequences, and it is not usually what determines whether a project succeeds. What determines that is whether the target variable corresponds to the decision, whether the features will be available at the moment the prediction is needed, whether the evaluation split respects the structure of the data, and whether the metric being optimised is the one the business cares about. A project can fail every one of those tests and still produce a model with an impressive score, which is precisely why the score is not the deliverable.

The scope covered here runs from the classical supervised methods through feature construction, evaluation, the specific problem of rare events, unsupervised and anomaly detection where there are no labels at all, the foundations of neural networks, forecasting over time, and ranking and recommendation. What is deliberately excluded is worth naming. Moving and shaping data at scale — pipelines, warehousing, Spark, orchestration — is data engineering and lives in Data & AI Engineering. Everything required to get a trained model into production and keep it working is MLOps & ML Platform. And building product features on top of a language model you did not train is a separate discipline again, in Generative AI & Large Language Models.

That last boundary needs stating plainly because the vocabulary has blurred. Prompting a large model is not machine learning in the sense examined here: there is no training set, no held-out split, no metric selection tied to a loss function, and no leakage risk of the kind that dominates this section. Both are real disciplines. They are interviewed separately, hired separately, and confusing them wastes preparation. The overlap is genuine only in two places — evaluation discipline, and the deep learning foundations that explain what a transformer is doing.

A note on this site's structure. The older Data & AI Engineering section still carries ML Fundamentals and Statistics & Experimentation subsections from earlier waves, and those pages remain. ML Fundamentals is a compressed treatment of what this section now covers at length. Statistics & Experimentation — hypothesis testing, A/B design, reading a result honestly — has deliberately not been duplicated here, so if you need that material it is still the right place to go.