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Advanced supervised ML: calibration, class hierarchies, multi-label vs multiclass, sample weights, label smoothing, distillation, ordinal and cost-sensitive learning, kernel methods, and production pitfalls. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Intermediate supervised ML: regularization, class imbalance, decision boundaries, logistic and softmax outputs, feature scaling, encoding categoricals, tree-based learners, and training pitfalls. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of supervised machine learning: labeled data, classification vs regression, training and prediction, common losses, overfitting, and basic model families. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
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