Machine Learning

(1)H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform to apply machine learning models on big data. It support R, Python, and Java… users. You can also use h2o4GPU to take advantage of GPUs to build advanced machine learning models. Here is a link for h2o tutorial:
(2)The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie: a comprehensive and rigorous book for Machine Learning. This book is more mathematically intensive than other books. If you are more interested in applying Machine Learning without working too much with maths, you can try (3)An Introduction to Statistical Learning: With Applications in R by Gareth M. James, Trevor Hastie, Daniela Witten, Robert Tibshirani. After working with (3), you can move to use H2O.

R books

R for Data Science by Hadley Wickham and Garrett Grolemund. This book show you how to use R to work with your data from import, tidy to explore (tranform, visualize, …) your data. Tidyverse and ggplot2 are the two most important pacakges that you can learn how to use from this book
Advanced R. A more advanced book than R for Data Science to learn R.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. A comprehensive book that teaches you from Learner Regression, SVM, Decision Tree,… to Neural Network and Deep Learning.