Artificial Intelligence and Machine Learning Made Simple

Explore the three branches of machine learning—supervised, unsupervised, and reinforcement—along with classification, regression, clustering, and trial‑and‑error learning for robotics and self‑driving cars.

Learn supervised learning via regression by predicting house prices from size with linear regression, using training and testing splits in Python on the Ames housing data.

Compare regression models using metrics like mean absolute error, mean squared error, root mean squared error, and R2 score to evaluate predictions against actual values in test data.

Explore supervised learning through binary classification using a credit approval example. See how algorithms like logistic regression, support vector machines, decision trees, and random forest separate approved from rejected loans.

Evaluate a pneumonia detector with a test dataset using a confusion matrix, exploring accuracy, precision, recall, and F1 score to balance classification in unbalanced data.

Discover unsupervised learning techniques that infer patterns from unlabeled data, including clustering, anomaly detection, association mining, and dimensionality reduction using feature selection, feature extraction, and PCA.

Explore how deep learning uses multi-layer neural networks to learn from data, especially unstructured data, by training perceptron-based models and mapping inputs to outputs.

Reinforcement learning uses trial and error to learn goal-oriented behavior from delayed returns, with agents, actions, and environments, often enhanced by deep neural networks to achieve superhuman performance.