Springer Nature provides many books on Machine Learning, data analysis and statistics
See below a non-exhaustive list of these books
An Introduction to Machine Learning
Miroslav Kubat
Introduction to Statistics and Data Analysis
Christian Heumann, Michael Schomaker, Shalabh
A Modern Introduction to Probability and Statistics
F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
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
Introductory Time Series with R
Paul S.P. Cowpertwait, Andrew V. Metcalfe
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
Principles of Data Mining
Max Bramer
Data Mining
Charu C. Aggarwal
Robotics, Vision and Control
Peter Corke
Statistical Analysis and Data Display
Richard M. Heiberger, Burt Holland
Stochastic Processes and Calculus
Uwe Hassler
Statistical Analysis of Clinical Data on a Pocket Calculator
Ton J. Cleophas, Aeilko H. Zwinderman
The Data Science Design Manual
Steven S. Skiena
Guide to Discrete Mathematics
Gerard O’Regan
Multivariate Calculus and Geometry
Seán Dineen
Statistics and Analysis of Scientific Data
Massimiliano Bonamente
Modelling Computing Systems
Faron Moller, Georg Struth
Linear Algebra
Jörg Liesen, Volker Mehrmann
Algebra
Serge Lang
Applied Linear Algebra
Peter J. Olver, Chehrzad Shakiban
Understanding Analysis
Stephen Abbott
Linear Programming
Robert J Vanderbei
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
Regression Modeling Strategies
Frank E. Harrell , Jr.
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
Digital Image Processing
Wilhelm Burger, Mark J. Burge
Bayesian Essentials with R
Jean-Michel Marin, Christian P. Robert
Introduction to Artificial Intelligence
Wolfgang Ertel
Introduction to Deep Learning
Sandro Skansi
Neural Networks and Deep Learning
Charu C. Aggarwal
Data Science and Predictive Analytics
Ivo D. Dinov
Excel Data Analysis
Hector Guerrero
A Beginners Guide to Python 3 Programming
John Hunt
Advanced Guide to Python 3 Programming
John Hunt
