Common Data Science & ML Tasks
Connect business questions to common analytical task types such as classification, regression, clustering, anomaly detection, dimensionality reduction and forecasting. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
ClassificationClassification predicts one of a finite set of classes, often with probabilities or scores before a final class decision. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02RegressionRegression predicts a continuous numeric quantity. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03ClusteringClustering groups observations according to similarity or density without known class labels. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Anomaly detectionAnomaly detection identifies observations that are unusual relative to normal behaviour or the learned data distribution. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Dimensionality reductionDimensionality reduction maps many input variables into a smaller representation while preserving selected structure. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
06ForecastingForecasting predicts future values while respecting temporal order, trend, seasonality and changing relationships. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.