Machine Learning for Humans

For newcomers to the field of artificial intelligence, prioritizing among endless AI resources can be an overwhelming challenge. This list…

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).