Machine Learning / Prediction and data foundations
Machine Learning β Prediction and data foundations
Tasks, data quality, splits, preprocessing, regression, and classification.
What you will learn
- Tasks, data quality, splits, preprocessing, regression, and classification.
Problem framing and useful baselines
Turn an operational question into a prediction contract, then compare learning with a simple defensible alternative.
Start learningData quality and feature contracts
Inspect meaning, alignment, missingness, and measurement policy before fitting a model to apparently tidy arrays.
Start learningSplits, groups, time, and leakage
Estimate generalization under the intended deployment setting and prevent information from crossing evaluation boundaries.
Start learningPreprocessing without leakage
Learn transformations from training data, preserve the feature schema, and apply the identical fitted policy to future inputs.
Start learningRegression, loss, and linear models
Fit a transparent regression example and evaluate errors in meaningful units without confusing training fit with generalization.
Start learningClassification scores and metrics
Separate ranking, probabilities, decisions, and error rates so a classification result has an interpretable operational meaning.
Start learning