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Awesome Artificial Intelligence

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

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A seven day bootcamp designed in MIT to introduce deep learning methods and applications

A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more

A free deep reinforcement learning course by OpenAI

Beginner's course to learn deep learning and neural networks without frameworks.

Learn the Fundamentals of AI. Course run by Peter Norvig

The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems

This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics

Basic machine learning algorithms for supervised and unsupervised learning

Introductory course on machine learning focusing on: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.

Georgia Tech's course on Artificial Intelligence focussing on Symbolic AI.

Deep Reinforcement Bootcamp Lectures - August 2017

Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

This is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding.

In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning.

divided by each chapter in "Artificial Intelligence: A Modern Approach".

Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems

This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.

Written for non-specialists, it covers the discipline's foundations, major theories, and principal research areas, plus related topics such as artificial life

In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work

Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI

Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Also audio version available from

Kurzweil discusses how the brain works, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems

Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.

Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.

Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.

PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.

Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.

Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.

Fusion in Action teaches you to build a full-featured data analytics pipeline, including document and data search and distributed data clustering.

Early access book on how to create practical NLP applications using Python.

Early access book that introduces the most valuable machine learning techniques.

An introduction to managing successful AI projects and applying AI to real-life situations.

An Introduction to AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required.

A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.


Early access book that provides basics of machine learning and using R programming language.

a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.

a book for machine learning engineers on how to train and deploy machine learning systems on public clouds like AWS, Azure, and GCP, using a code-oriented approach.



Superintelligence asks the questions: What happens when machines surpass humans in general intelligence. A really great book.

Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?

Ray Kurzweil, director of engineering at Google, explored the process of reverse-engineering the brain to understand precisely how it works, then applies that knowledge to create vastly intelligent machines.

The 1980 paper by philospher John Searle that contains the famous 'Chinese Room' thought experiment. Probably the most famous attack on the notion of a Strong AI possessing a 'mind' or a 'consciousness', and interesting reading for those interested in the intersection of AI and philosophy of mind.

Written by Douglas Hofstadter and taglined "a metaphorical fugue on minds and machines in the spirit of Lewis Carroll", this wonderful journey into the the fundamental concepts of mathematics,symmetry and intelligence won a Pulitzer Price for Non-Fiction in 1979. A major theme throughout is the emergence of meaning from seemingly 'meaningless' elements, like 1's and 0's, arranged in special patterns.

Max Tegmark, professor of Physics at MIT, discusses how Artificial Intelligence may affect crime, war, justice, jobs, society and our very sense of being human both in the near and far future.

Free Content

This book is published by Cambridge University Press, 2010

This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers.

This course provides a broad introduction to machine learning and statistical pattern recognition.

The book covers computer simulation of human activities, such as problem solving and natural language understanding; computer vision; AI tools and techniques; an introduction to AI programming; symbolic and neural network models of cognition; the nature of mind and intelligence; and the social implications of AI and cognitive science.

Marvin Minsky's seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis.

This book is designed to help preservice and inservice teachers learn about some of the educational implications of current uses of Artificial Intelligence as an aid to solving problems and accomplishing tasks.

a book by Bill Hibbard that combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence.

a cluster of pages on artificial intelligence and machine learning.

A website with explanations on topics from Machine Learning to Statistics. All helped with beautiful animated infographics and real life examples. Available in various languages.


Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.


Fast Artifical Neural Network library; an implementation of neural networks. GNU GPL2.1


This interactive liveVideo course gives you a crash course in using AWS for machine learning, teaching you how to build a fully-working predictive algorithm.

Deep Learning with R in Motion teaches you to apply deep learning to text and images using the powerful Keras library and its R language interface.

Grokking Deep Learning in Motion will not just teach you how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!

This liveVideo breaks down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents.


A curated list of awesome Machine Learning frameworks, libraries and software.


Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier

1y 6m

A collection of important graph embedding, classification and representation learning papers with implementations.


A curated list of community detection research papers with implementations.


A collection of research papers on decision, classification and regression trees with implementations.


A curated list of gradient boosting research papers with implementations.


A curated list of data mining papers about fraud detection.


A curated list of awesome neural network-based art resources.

2y 46d

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning

This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach

about helping professional programmers to confidently apply machine learning algorithms to address complex problems.

A collection of free professional and in depth Machine Learning and Data Science video tutorials and courses

A collection of free professional and in depth Artificial Intelligence video tutorials and courses

A collection of free professional and in depth Deep Learning video tutorials and courses

Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.





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Large aggregation of AI resources

Directory of open source software and open access data for the AI research community