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Springer has released 65 Machine Learning and Data books for free

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 Peter J. Olver

Methods of Mathematical Modelling Thomas Witelski, Mark Bowen

LaTeX in 24 Hours Dilip Datta

Introduction to Statistics and Data Analysis Christian Heumann, Michael Schomaker, Shalabh

Principles of Data Mining Max Bramer

Computer Vision Richard Szeliski

Data Mining Charu C. Aggarwal

Computational Geometry Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars

Robotics, Vision and Control Peter Corke

Statistical Analysis and Data Display Richard M. Heiberger, Burt Holland

Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson

Stochastic Processes and Calculus Uwe Hassler

Statistical Analysis of Clinical Data on a Pocket Calculator Ton J. Cleophas, Aeilko H. Zwinderman

Clinical Data Analysis on a Pocket Calculator Ton J. Cleophas, Aeilko H. Zwinderman

The Data Science Design Manual Steven S. Skiena

An Introduction to Machine Learning Miroslav Kubat

Guide to Discrete Mathematics Gerard O’Regan

Introduction to Time Series and Forecasting Peter J. Brockwell, Richard A. Davis

Multivariate Calculus and Geometry Seán Dineen

Statistics and Analysis of Scientific Data Massimiliano Bonamente

Modelling Computing Systems Faron Moller, Georg Struth

Search Methodologies Edmund K. Burke, Graham Kendall

Linear Algebra Done Right Sheldon Axler

Linear Algebra Jörg Liesen, Volker Mehrmann

Algebra Serge Lang

Understanding Analysis Stephen Abbott

Linear Programming Robert J Vanderbei

Understanding Statistics Using R Randall Schumacker, Sara Tomek

An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Statistical Learning from a Regression Perspective Richard A. Berk

Applied Partial Differential Equations J. David Logan

Robotics Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo

Regression Modeling Strategies Frank E. Harrell , Jr.

A Modern Introduction to Probability and Statistics F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester

The Python Workbook Ben Stephenson

Machine Learning in Medicine — a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman

Object-Oriented Analysis, Design and Implementation Brahma Dathan, Sarnath Ramnath

Introduction to Data Science Laura Igual, Santi Seguí

Applied Predictive Modeling Max Kuhn, Kjell Johnson

Python For ArcGIS Laura Tateosian

Concise Guide to Databases Peter Lake, Paul Crowther

Digital Image Processing Wilhelm Burger, Mark J. Burge

Bayesian Essentials with R Jean-Michel Marin, Christian P. Robert

Robotics, Vision and Control Peter Corke

Foundations of Programming Languages Kent D. Lee

Introduction to Artificial Intelligence Wolfgang Ertel

Introduction to Deep Learning Sandro Skansi

Linear Algebra and Analytic Geometry for Physical Sciences Giovanni Landi, Alessandro Zampini

Applied Linear Algebra Peter J. Olver, Chehrzad Shakiban

Neural Networks and Deep Learning Charu C. Aggarwal

Data Science and Predictive Analytics Ivo D. Dinov

Analysis for Computer Scientists Michael Oberguggenberger, Alexander Ostermann

Excel Data Analysis Hector Guerrero

A Beginners Guide to Python 3 Programming John Hunt

Advanced Guide to Python 3 Programming John Hunt