Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.
The LION Way: Machine Learning plus Intelligent Optimization
Roberto Battiti & Mauro Brunato, 2013
Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.
Real-Time Big Data Analytics: Emerging Architecture
Mike Barlow, 2013
Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.
Disruptive Possibilities: How Big Data Changes Everything
Jeffrey Needham, 2013
This book provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds.
Richard Szeliski, 2010
Challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which you can use on you own personal media
Natural Language Processing with Python
Steven Bird, 2009
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
The Elements of Data Analytic Style
Associate Professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health
Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks.
Algorithms for Reinforcement Learning
Csaba Szepesvari , 2009
This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods. This is everything a graduate student could ask for in a text.
A Programmer’s Guide to Data Mining
Ron Zacharski, 2015
A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. This work is licensed under a Creative Commons license.
Bayesian Reasoning and Machine Learning
David Barber, 2014
For final-year undergraduates and master’s students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.
Data Mining and Analysis: Fundamental Concepts and Algorithms
Mohammed J. Zaki & Wagner Meria Jr., 2014
The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics.
Data Mining with Rattle and R
Graham Williams, 2011
This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built.