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Springer has released 65 Machine Learning and Data books for free

Hundreds of books are now free to download



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.

If you are looking for more recommended books about Machine Learning and data you can check my previous article about it.


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


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


Source: Paper.li

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