Machine Learning Fundamentals — Study Notes

A comprehensive, exam-focused guide to introductory Machine Learning, covering the supervised/unsupervised/reinforcement learning paradigms, linear and logistic regression, decision trees, k-nearest neighbors and k-means clustering, the bias-variance tradeoff, overfitting/underfitting and regularization, classification/regression evaluation metrics (precision, recall, F1), cross-validation, neural network fundamentals, and feature engineering (including one-hot encoding). Includes custom diagrams (a bias-variance tradeoff curve and a neural network architecture illustration), fully worked problems (overfitting/underfitting diagnosis, evaluation metric selection, cross-validation reasoning), comparison tables clarifying commonly confused ML concepts (logistic regression vs. linear regression, precision vs. recall), an 11-term glossary, and original practice questions with detailed explanations. Built around the practical diagnostic reasoning (bias-variance, metric selection) that distin