Learn machine learning basics
Short lessons written around the screening tests employers use. Read one, then take the test.
Splitting data, overfitting and the bias-variance trade-offHow train, validation and test sets work, how to spot overfitting and underfitting, and how cross-validation helps.Lesson 1
Choosing and calculating evaluation metricsAccuracy, precision, recall, F1 and RMSE explained with worked numbers, and how to pick the right one for the task.Lesson 2
Core algorithms: regression, trees, k-nearest neighbours and k-meansWhat each common algorithm does in plain terms, which problem type it fits, and the questions screenings ask about them.Lesson 3
Preparing features: encoding, scaling, missing values and avoiding leakageHow to turn raw columns into model inputs, why preprocessing must be fitted on training data only, and how leakage inflates scores.Lesson 4
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