65 of the most popular machine learning and data science books are now available for free download.


Springer has made hundreds of free books available to the general public on a number of topics. The total number of books on the list is 408. They cover a broad variety of scientific and technical topics. To save you time, I’ve compiled a list of all the books (65 in total) that are important to the field of data and machine learning. Books on the statistical side of the domain (Algebra, Statistics, and more) are included, as well as more specialized books on Deep Learning and other advanced topics. You can also find good books in a number of programming languages, such as Python, R, and MATLAB, among others.

The 65 Machine Learning & Data Science Books List:
The Elements of Statistical Learning

Trevor Hastie, Robert Tibshirani, Jerome Friedman

Introductory Time Series with R
Paul S.P. Cowperthwait, 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

Modeling 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

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

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

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