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MIT: Intro to Deep Learning - A seven day bootcamp designed in MIT to introduce deep learning methods and applications
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
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
Machine Learning - Basic machine learning algorithms for supervised and unsupervised learning
Deep Learning - An Introductory course to the world of Deep Learning.
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.
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.
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.
Deep Learning Crash Course In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning.
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.
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.
Kaggle's micro courses - A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.