Machine Learning for Humans

Simple, plain-English explanations accompanied by math, code, and real-world examples.


Part 1: Why Machine Learning MattersThe big picture of artificial intelligence and machine learning — past, present, and future.

Part 2.1: Supervised LearningLearning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent.

Part 2.2: Supervised Learning IITwo methods of classification: logistic regression and SVMs.

Part 2.3: Supervised Learning IIINon-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.

Part 3: Unsupervised LearningClustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).

Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.

Part 5: Reinforcement LearningExploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.

Appendix: The Best Machine Learning ResourcesA curated list of resources for creating your machine learning curriculum.