Foundations of Machine Learning

Define machine learning and distinguish it from traditional programming

Distinguish between supervised, unsupervised, and reinforcement learning

Explain the concepts of features, labels, training data, and models

Split a dataset into training and testing sets and explain the rationale

Use scikit-learn to load a dataset and prepare it for training

Define overfitting and underfitting and explain the bias-variance tradeoff

Select and apply appropriate evaluation metrics for a given problem (e.g., accuracy, MSE)

Explain the purpose of k-fold cross-validation for robust model evaluation