Machine Learning for Humans

This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series.
If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc. Take a look at our top picks in the Appendix: The Best Machine Learning Resources.

Table of Contents

Part 1: Introduction. The big picture of artificial intelligence and machine learning — past, present, and future.
Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent.
Part 2.2: Supervised Learning II. Two methods of classification: logistic regression and support vector machines (SVMs).
Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.
Part 3: Unsupervised Learning. Clustering: 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 Learning. Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.
Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.