ML Fundamentals Skills Tests
Core ML concepts for practitioners
Tests
ML Fundamentals — Beginner
Supervised vs unsupervised, splits, overfitting, labels, classification vs regression, metrics basics, loss, gradient descent, and cross-validation.
BeginnerOpenML Fundamentals — Intermediate
Regularization, ROC/F1, imbalance, scaling, encodings, ensembles, leakage, early stopping, losses, softmax, embeddings, and tuning discipline.
IntermediateOpenML Fundamentals — Advanced
Bias–variance, calibration, interpretability caveats, imbalance metrics, deep learning training dynamics, transfer/domain shift, causality limits, nested CV, and drift.
AdvancedOpen