For newcomers to the field of artificial intelligence, prioritizing among endless AI resources can be an overwhelming challenge. This list attempts to do exactly that: it’s a carefully curated compilation of resources for getting up to speed quickly on key topics in artificial intelligence research and its long-term implications.

The list is divided into “80/20” sections with a few high-priority readings, for maximum value with minimal time investment, and “deep dive” sections for further exploration.

Readers need not be technical, nor have a prior background in artificial intelligence. The list may be of special interest to those considering entering the field of AI research or adjacent fields, whether in technical or non-technical roles.

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

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This series is available as a full-length e-book! Download here. Free for download, contributions appreciated (paypal.me/ml4h)


Part 1: Why Machine Learning Matters. 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 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. …

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

This article is an addendum to the series Machine Learning for Humans 🤖👶, a guide for getting up-to-speed on machine learning concepts in 2-3 hours.

General advice on crafting a curriculum

Going to school for a formal degree program for isn’t always possible or desirable. For those considering an autodidactic alternative, this is for you.

1. Build foundations, and then specialize in areas of interest.

You can’t go deeply into every machine learning topic. There’s too much to learn, and the field is advancing rapidly. …


Vishal Maini

Strategy & communications @DeepMindAI. Previously @Upstart, @Yale, @TrueVenturesTEC. Views expressed here are my own.

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