Machine Learning for Humans

The ultimate guide to machine learning. Simple, plain-English explanations accompanied by math, code, and real-world examples.

A compendium of resources for crafting a curriculum on artificial intelligence, machine learning, and deep learning.

Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning.

Where, why, where, and how deep neural networks work. Drawing inspiration from the brain. CNNs and RNNs. Real-world applications.

Clustering and dimensionality reduction: k-means clustering, hierarchical clustering, PCA, SVD.

Non-parametric models: k-nearest neighbors, decision trees, and random forests. Introducing cross-validation and ensemble models.

Classification with logistic regression and support vector machines (SVMs).

The two tasks of supervised learning: regression and classification. Linear regression, loss functions, and gradient descent.