Table of Contents

Springer has released 65 Machine Learning and Data books for free


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 books, 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.

The 65 books list:

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


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

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

Taken from Medium Post 


Uri Eliabayev

AI Consultant, Product Manager, Community Manager


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