Here are some really useful resources to have a headstart in different important topics related to machine learning and other subdomains.
Publications
Books in core Artificial Intelligence
 The AI bible Artificial Intelligence: A Modern Approach (AIMA)
 Look into GitHub for coding solutions of AIMA in here
Books in Reinforcement Learning
 Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
 Look into GitHub for coding solutions in Python of this book in here
Books in Deep Learning
Books in Machine Learning

In Python
 Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
 Look into GitHub for coding solutions in Python of this book in here
 Machine Learning in Action by Peter Harrington
 Look into GitHub for coding solutions in Python of this book in here
 Machine Learning with Python/ScikitLearn – Application to the Estimation of Occupancy and Human Activities
2. In R (Based on statistical learning)
 The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
 Look into GitHub for coding solutions in R of this book in here
 An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
 Look into GitHub for coding solutions in R of this book in here
 Statistical Learning with Sparsity The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani and Martin Wainwright
3. In MATLAB
 Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy
 Look into GitHub for coding solutions in both MATLAB and Python of this book in here
4. In WEKA
 Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten and Eibe Frank in here and here
 Look into this elaborated appendix of WEKA for this book in here
Books in Pattern Recognition
 Pattern Classification (2nd ed.) by R. O. Duda, P. E. Hart and D. G. Stork
 Get the official solution manual of this book in here and codes in MATLAB in here
 Pattern Recognition 4th Edition by Sergios Theodoridis and Konstantinos Koutroumbas
 Get the coding solutions of this book in MATLAB in here
 Support Vector Machines for Pattern Classification by Shigeo Abe
Some great blogs
 Andrej Karpathy blog
 Sebastian Ruder
 Colah’s blog
 WILDML – Artificial Intelligence, Deep Learning, and NLP
 Distill
Topicspecific lectures and video tutorials
 Machine Learning by Andrew Ng
 MIT OpenCourseWare – Computational Science & Engineering 1,2
 Convolutional Neural Networks for Visual Recognition
 The Learning Problem – Introduction; supervised, unsupervised, and reinforcement learning
 Neural Networks for Machine Learning taught by Geoffrey Hinton
 Neural networks class – Université de Sherbrooke
 Deep Learning for Natural Language Processing
 Oxford Deep NLP 2017 course
In addition to the abovementioned resources, there are some extremely important areas AI share and extend concepts with. Three most useful of them are Linear Algebra, Calculus (with extensions to multivariate calculus) and Probability and Statistics. Complete understanding and reasoning of these areas will help you grasp many involved topics of neural networks, deep learning, computer vision etc. Below are some great books and online resources you should check in order to master them.
Linear Algebra

Books
 Fundamentals of Linear Algebra by James B. Carrell
 Linear Algebra by David Cherney, Tom Denton and Rohit Thomas
2. Video Tutorials
Calculus

Books
 Mathematical Methods for Computer Vision, Robotics, and Graphics by Justin Solomon
 Calculus Made Easy by Silvanus P. Thompson and Martin Gardner
2. Video Tutorials
Probability and Statistics

Books
 Introduction to Probability and Statistics by William Mendenhall, Robert Beaver, Barbara Beaver, S. Ahmed
 Get the student solution manual of this book in here
 Basic Probability Theory by Robert B. Ash
 Probability Theory: The Logic of Science by E. T. Jaynes
2. Video Tutorials
The best way to learn a concept is to apply them to solve a real problem with handson programming. In the fields of AI, you will have to work with a large amount of data, in more advanced level, big data. Therefore, decent idea of software engineering including parallel programming, how to work with CPU, GPU and TPU (Tensor Processing Unit) etc. are equally important. Below are some resources where you will get firm knowledge about these stuffs.
Parallel Programming (In Python)
 Effective Python: 59 Specific Ways to Write Better Python by Brett Slatkin
 An introduction to parallel programming using Python’s multiprocessing module by Sebastian Raschka
GPU accelerated Machine Learning
 A Full Hardware Guide to Deep Learning by Tim Dettmers
 Building your own deep learning box
 Intro to Parallel Programming Using CUDA to Harness the Power of GPUs
 CUDA by Example – An Introduction to Generalpurpose GPU Programming
 Check out some GPUbased frameworks written in Python and CUDA – Theano, Tensorflow, PyTorch
TPU accelerated Machine Learning
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