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126 Free Artificial Intelligence (AI) Courses, Ebooks, Videos and Papers

126 Free Artificial Intelligence (AI) Courses, Ebooks, Videos and Papers
Artificial intelligence (AI)is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers “smart”. They work on their own without being encoded with commands. The basic goal of AI is to enable computers and machines to perform intellectual tasks such as problem solving, decision making, perception, and understanding human communication. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time.

Even if you don’t have any prior experience in engineering, you can learn artificial intelligence from home and start applying your knowledge in practice, creating simple machine learning solutions and making first steps towards your new profession. Machine Learning is the subset of Artificial Intelligence (AI) that enables computers to learn and perform tasks they haven’t been explicitly programmed to do.

This is a curated list of free Artificial Intelligence (AI) courses, ebooks, videos and papers pointing towards interesting directions and topics that you may be interested in. Some resources may be old, but still applicable to today’s standards of AI implementations. We’ve also included some of our previous compilations of AI ebooks and resources, so feel free to check them out as well.

Past Compilations

  1. 42 Most Popular and Downloaded Artificial Intelligence, Logic & Robotics Ebooks
  2. 16 Sites With Free Artificial Intelligence Ebooks
  3. 140 Awesome Free Ebooks and Tutorials for You to Learn Python
  4. Free Python ebooks and resources
  5. Free Artificial Intelligence ebooks and resources


  1. Amazon Machine Learning Developer Guide
    A book for ML developers which itroduces the ML concepts & strategies with lots of practical usages.
  2. Artificial Intelligence For Robotics
    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
  3. CS50’s Intro to Artificial Intelligence
    This course explores the concepts and algorithms at the foundation of modern artificial intelligence.
  4. Deep Blueberry: Deep Learning book
    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.
  5. Deep Learning
    An Introductory course to the world of Deep Learning using TensorFlow.
  6. Deep Learning
    Goodfellow, Bengio and Courville’s introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
  7. Deep RL Bootcamp Lectures
    Deep Reinforcement Bootcamp Lectures – August 2017.
  8. EdX Artificial Intelligence
    The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems.
  9. Elements of AI (Part 1) – Reaktor/University of Helsinki
    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.
  10. Intro to Artificial Intelligence
    Learn the Fundamentals of AI. Course run by Peter Norvig.
  11. Kaggle’s micro courses
    A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.
  12. Knowledge Based Artificial Intelligence
    Georgia Tech’s course on Artificial Intelligence focussing on Symbolic AI.
  13. Machine Learning
    Basic machine learning algorithms for supervised and unsupervised learning.
  14. Machine Learning Crash Course by Google Machine Learning Crash
    Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
  15. Machine Learning for Humans
    A series of simple, plain-English explanations accompanied by math, code, and real-world examples.
  16. MIT Artifical Intelligence Videos
    MIT AI Course.
  17. Python Class by Google
    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.
  18. Reinforcement Learning: An Introduction
    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.
  19. Spinning Up in Deep Reinforcement Learning
    A free deep reinforcement learning course by OpenAI.
  20. Stanford Deep Learning Series
    CS230: Deep Learning / Autumn 2018.
  21. Stanford Statistical 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.
  22. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    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.


  1. How Machine Learning Works
    Mostafa Samir. Early access book that introduces machine learning from both practical and theoretical aspects in a non-threating way.


  1. Python Tools for Machine Learning
  2. Python for Artificial Intelligence


  1. Life 3.0: Being Human in the Age of Artificial Intelligence
    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.
  2. Minds, Brains, And Programs
    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.

Past Related Posts

  1. 140 Awesome Free Ebooks and Tutorials for You to Learn Python – 2021
    These resources will cover Django, Flask, Massive Open Online Courses (MOOC), Tutorials, other Python Resources and a list of websites in which you can test out your skills.
  2. Other Free Python Ebooks & Resources
  3. Other Free Artificial Intelligence Ebooks & Resources
  4. Other Programming Ebooks & Resources

Free Content

  1. Artificial Intelligence and Molecular Biology
    The current volume is an effort to bridge that range of exploration, from nucleotide to abstract concept, in contemporary AI/MB research.
  2. Brief Introduction To Educational Implications Of Artificial Intelligence
    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.
  3. Computers and Thought: A practical Introduction to Artificial Intelligence
    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.
  4. Encyclopedia: Computational intelligence
    Scholarpedia is a peer-reviewed open-access encyclopedia written and maintained by scholarly experts from around the world.
  5. Ethical Artificial Intelligence
    A book by Bill Hibbard that combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence.
  6. Foundations Of Computational Agents
    This book is published by Cambridge University Press, 2010
  7. Golden Artificial Intelligence
    A cluster of pages on artificial intelligence and machine learning.
  8. Modeling Agents with Probabilistic Programs
    This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning.
  9. R2D3
    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.
  10. Society of Mind
    Marvin Minsky’s seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis.
  11. Stanford CS229 – Machine Learning
    This course provides a broad introduction to machine learning and statistical pattern recognition.
  12. The Quest For Artificial Intelligence
    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.


  1. AIMACode
    Source code for “Artificial Intelligence: A Modern Approach” in Common Lisp, Java, Python. More to come.
  2. ExplainX
    ExplainX is a fast, light-weight, and scalable explainable AI framework for data scientists to explain any black-box model to business stakeholders.
  3. FARGonautica
    Source code of Douglas Hosftadter’s Fluid Concepts and Creative Analogies Ph.D. projects.


  1. A tutorial on Deep Learning
    Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and “products of experts” are obtained by multiplying the densities together and renormalizing.
  2. Basics of Computational Reinforcement Learning
    In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a system’s ability to make behavioral decisions. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study.
  3. Deep Reinforcement Learning
    In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning.
  4. Intelligent agents and paradigms for AI
    This is a lecture video for the Carnegie Mellon course: ‘Graduate Artificial Intelligence’, Spring 2014.
  5. The Unreasonable Effectiveness Of Deep Learning
    The Director of Facebook’s AI Research, Dr. Yann LeCun gives a talk on deep convolutional neural networks and their applications to machine learning and computer vision.


  1. Deep Learning
    Yoshua Bengio, Ian Goodfellow and Aaron Courville put together this currently free (and draft version) book on deep learning. The book is kept up-to-date and covers a wide range of topics in depth (up to and including sequence-to-sequence learning).
  2. Deep Learning. Methods And Applications Free book from Microsoft Research
    This book is aimed to provide an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks.
  3. Getting Started with Deep Learning and Python
    In this blog post we’ll review an example of using a Deep Belief Network to classify images from the MNIST dataset, a dataset consisting of handwritten digits.
  4. Introduction to Machine Learning
    Introductory level machine learning crash course.
  5. Machine Learning Mastery
    Machine learning course that explains things clearly rather than writing everything as if it were an academic paper.
  6. Neural Networks And Deep Learning
    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


  1. Adversarial Networks
    Framework for generation.
  2. Neural Turing Machine
    We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
  3. Representation learning
    Survey paper on representation methods.
  4. Tensorflow
    Google’s large scale infrastructure project.

RNN structures

  1. Deep Speech – Speech implementation.
  2. End to End Memory networks – Facebook’s memory storage.
  3. LTSM – Long term short term memory.
  4. Memory Networks – On adding memory storage.
  5. Neural Programmer – On adding basic artithmetic operations.
  6. Spatial Transformer – DeepMind digit classification.

Word Vectors

  1. Adaptive skip-gram
    Similar approach, with adaptive properties.
  2. Infinite Dimensional Word Embeddings
    Describe a method for learning word embeddings with data-dependent dimensionality.
  3. sense2vec
    On word sense disambiguation.
  4. Skip Thought Vectors
    Word representation method.
  5. word2vec
    On creating vectors to represent language, useful for RNN inputs.

Natural Language

  1. LTSM over tree structures
    The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process.
  2. Neural autocoder for paragraphs and documents
    LTSM representation.
  3. Sequence to Sequence Learning
    Word vectors for machine translation.
  4. Teaching Machines to Read and Comprehend
    DeepMind paper.

Convolutional Neural Nets

  1. A Neural Algorithm of Artistic Style – Popular papeer.
  2. DRAW – An RNN for image classfication.
  3. Generative Adversarial Networks – Unsupervised learning to generate images.
  4. ImageNet Classification – Popular paper.


  1. K-Means with Tensorflow
  2. LTSM RNN in Python
  3. Tensorflow Tutorials


  1. DeepMind Q&A Corpus


  1. AI & Society
  2. AI Communications
  3. AI Magazine
  4. Annals of Mathematics and Artifical Intelligence
  5. Applicable Algebra in Engineering, Communication and Computing
  6. Applied Artificial Intelligence
  7. Applied Intelligence
  8. Artificial Intelligence
  9. Artificial Intelligence for Engineering Design, Analysis and Manufacturing
  10. Artificial Intelligence Review
  11. Automated Software Engineering
  12. Autonomous Agents and Multi-Agent Systems
  13. Computational and Mathematical Organization Theory
  14. Electronic Transactions on Artificial Intelligence
  15. Evolutionary Intelligence
  16. EXPERT—IEEE Intelligent Systems
  17. IEEE Transactions Automation Science and Engineering
  18. Intelligent Industrial Systems
  19. International Journal of Intelligent Systems
  20. International Journal on Artificial Intelligence Tools
  21. Journal of Artificial Intelligence Research
  22. Journal of Automated Reasoning
  23. Journal of Experimental and Theoretical Artificial Intelligence
  24. Journal of Intelligent Information Systems
  25. Journal on Data Semantics
  26. Knowledge Engineering Review
  27. Minds and Machines
  28. Progress in Artificial Intelligence


  1. AI Google
  2. Association For The Advancement of Artificial Intelligence
  3. Facebook AI
  4. Google DeepMind Research
  5. IBM Research
  6. IEEE Computational Intelligence Society
  7. Machine Intelligence Research Institute
  8. Microsoft Research
  9. Nvidia Deep Learning
  10. OpenAI


  1. AI Challenge
  2. MIT Battlecode


  1. AI Digest
    A weekly newsletter to keep up to date with AI, machine learning, and data science.


  1. AI Experiments with Google
    AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
  2. AIResources
    Directory of open source software and open access data for the AI research community
  3. AITopics
    Large aggregation of AI resources
  4. Artificial Intelligence Subreddit
    Reddit’s home for Artificial Intelligence.
  5. Open Cognition Project
    We’re undertaking a serious effort to build a thinking machine