• CS50’s Intro to Artificial Intelligence - This course explores the concepts and algorithms at the foundation of modern artificial intelligence
• Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI
• EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
• 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
• Knowledge Based Artificial Intelligence - Georgia Tech's course on Artificial Intelligence focussing on Symbolic AI.
• Python Class By Google
• Artificial Intelligence: A Modern Approach - Stuart Russell & Peter Norvig
• list of recommended reading
• Paradigms Of Artificial Intelligence Programming: Case Studies in Common Lisp - Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems
• 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.
• The Cambridge Handbook Of Artificial Intelligence - Written for non-specialists, it covers the discipline's foundations, major theories, and principal research areas, plus related topics such as artificial life
• Artificial Intelligence: A New Synthesis - Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI
• On Intelligence - 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 audible.com
• How To Create A Mind - 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
• 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.
• 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.
• Deep Learning and the Game of Go - 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 - Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
• Deep Learning with PyTorch - 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.
• Grokking Deep Reinforcement Learning - Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.
• Fusion in Action - Fusion in Action teaches you to build a full-featured data analytics pipeline, including document and data search and distributed data clustering.
• Real-World Natural Language Processing - Early access book on how to create practical NLP applications using Python.
• Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques.
• Succeeding with AI - An introduction to managing successful AI projects and applying AI to real-life situations.
• Essential Natural Language Processing - A hands-on guide to NLP with practical techniques, numerous Python-based examples and real-world case studies.
• Kaggle's micro courses - A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.
• Transfer Learning for Natural Language Processing - A book that gets you up to speed with the relevant ML concepts and then dives into transfer learning for NLP.
• Amazon Machine Learning Developer Guide - A book for ML developers which introduces the ML concepts & strategies with lots of practical usages.
• Machine Learning for Humans - A series of simple, plain-English explanations accompanied by math, code, and real-world examples.
• The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind - In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work
• Machine Learning Observability Course - Self-guided course covers the intuition, math, and best practices for effective machine learning observability.
• Deep Reinforcement Learning in Action - 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.
• 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.

Commerical Tools

• MIT Artificial Intelligence Videos - MIT AI Course
• Intro to Artificial Intelligence - Learn the Fundamentals of AI. Course run by Peter Norvig
• Machine Learning - Basic machine learning algorithms for supervised and unsupervised learning
• Deep RL Bootcamp Lectures - Deep Reinforcement Bootcamp Lectures - August 2017
• Deep Learning Crash Course
• Introduction to Artificial Intelligence (AI) - A high-level introduction to AI from IBM on Coursera
• Introduction to Generative AI - A beginner-level introduction to Generative AI from Google on Coursera
• Deep Learning - An Introductory course to Deep Learning using TensorFlow.
• 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.
• Knowledge Based Artificial Intelligence - Georgia Tech's course on Artificial Intelligence focussing on Symbolic AI.
• Grokking Deep Learning in Motion - Beginner's course to learn deep learning and neural networks without frameworks.
• Machine Learning Crash Course By Google - world case studies, and hands-on practice exercises.

Chat

• Chat GPT - to-use AI system. It allows users to engage in conversations, gain insights, automate tasks, and witness the future of AI all in one place.
• Claude

Images

• DALL·E 2 - language description.
• Midjourney

Video

• Sora - to-video AI model that can create realistic and imaginative scenes from text instructions.
• Runway