Springer has released hundreds of free books on a wide range of topics to the general public.
The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field.
Among the, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, and MATLAB, etc.
If you are looking for more recommended books about Machine Learning and data you can check my previous article about it.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Introductory Time Series with R
Paul S.P. Cowpertwait, Andrew V. Metcalfe
A Beginner’s Guide to R
Alain Zuur, Elena N. Ieno, Erik Meesters
Introduction to Evolutionary Computing
A.E. Eiben, J.E. Smith
Data Analysis
Siegmund Brandt
Linear and Nonlinear Programming
David G. Luenberger, Yinyu Ye
Introduction to Partial Differential Equations
David Borthwick
Fundamentals of Robotic Mechanical Systems
Jorge Angeles
Data Structures and Algorithms with Python
Kent D. Lee, Steve Hubbard
Introduction to Partial Differential Equations
Originally Publish at: https://towardsdatascience.com/