100+ Free Data Science Books

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.


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Comma delimit (e.g.,Python,Clustering)
Artificial Intelligence A Modern Approach, 1st Edition
4.2 (331 Ratings)
Artificial Intelligence

Artificial Intelligence A Modern Approach, 1st Edition

Stuart Russell, 1995

Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Learning Deep Architectures for AI
4.0 (18 Ratings)
Artificial Intelligence

Learning Deep Architectures for AI

Yoshua Bengio, 2009

Foundations and Trends(r) in Machine Learning.

The LION Way: Machine Learning plus Intelligent Optimization
3.8 (2 Ratings)
Artificial Intelligence

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.

Online Data Science Courses

Comprehensive list of top courses for 2018

Disruptive Possibilities: How Big Data Changes Everything
3.5 (109 Ratings)
Big Data

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.

Computer Vision
4.2 (98 Ratings)
Computer Science Topics

Computer Vision

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
Languages: Python
4.1 (448 Ratings)
Computer Science Topics

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.

Programming Computer Vision with Python
Languages: Python
4.0 (49 Ratings)
Computer Science Topics

Programming Computer Vision with Python

Jan Erik Solem, 2012

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, etc

The Elements of Data Analytic Style
3.6 (171 Ratings)
Data Analysis

The Elements of Data Analytic Style

Jeff Leek
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.

A Course in Machine Learning
Data Mining and Machine Learning

A Course in Machine Learning

Hal Daumé III, 2014
A First Encounter with Machine Learning
Data Mining and Machine Learning

A First Encounter with Machine Learning

Max Welling, 2011
Algorithms for Reinforcement Learning
4.1 (4 Ratings)
Data Mining and Machine Learning

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
Data Mining and Machine Learning

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
4.1 (161 Ratings)
Data Mining and Machine Learning

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 Algorithms In R
Languages: R
Data Mining and Machine Learning

Data Mining Algorithms In R

Wikibooks, 2014
Data Mining and Analysis: Fundamental Concepts and Algorithms
4.1 (11 Ratings)
Data Mining and Machine Learning

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: Practical Machine Learning Tools and Techniques
3.9 (156 Ratings)
Data Mining and Machine Learning

Data Mining: Practical Machine Learning Tools and Techniques

Ian H. Witten & Eibe Frank, 2005

Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

Data Mining with Rattle and R
Languages: R
4.1 (36 Ratings)
Data Mining and Machine Learning

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.

Deep Learning
Data Mining and Machine Learning

Deep Learning

Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

Gaussian Processes for Machine Learning
4.2 (79 Ratings)
Data Mining and Machine Learning

Gaussian Processes for Machine Learning

C. E. Rasmussen & C. K. I. Williams, 2006

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Information Theory, Inference, and Learning Algorithms
4.5 (380 Ratings)
Data Mining and Machine Learning

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay, 2005

"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions." Choice

Introduction to Machine Learning
Data Mining and Machine Learning

Introduction to Machine Learning

Amnon Shashua, 2008
Introduction to Machine Learning
Data Mining and Machine Learning

Introduction to Machine Learning

Alex Smola & S.V.N. Vishwanathan, 2008
KB – Neural Data Mining with Python Sources
Data Mining and Machine Learning

KB – Neural Data Mining with Python Sources

Roberto Bello, 2013
Machine Learning
Data Mining and Machine Learning

Machine Learning

Abdelhamid Mellouk & Abdennacer Chebira
Machine Learning, Neural and Statistical Classification
2.9 (1 Ratings)
Data Mining and Machine Learning

Machine Learning, Neural and Statistical Classification

D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999
Machine Learning – The Complete Guide
Data Mining and Machine Learning

Machine Learning – The Complete Guide

Wikipedia
Mining of Massive Datasets
4.4 (24 Ratings)
Data Mining and Machine Learning

Mining of Massive Datasets

Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

Modeling With Data
Data Mining and Machine Learning

Modeling With Data

Ben Klemens, 2008

Modeling with Data offers a useful blend of data-driven statistical methods and nuts-and-bolts guidance on implementing those methods. --Pat Hall, founder of Translation Creation

Neural Networks and Deep Learning
Data Mining and Machine Learning

Neural Networks and Deep Learning

Michael Nielsen, 2015

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you concepts behind neural networks and deep learning.

Bayesian Methods for Hackers
Languages: Python
4.1 (119 Ratings)
Data Mining and Machine Learning

Probabilistic Programming & Bayesian Methods for Hackers

Cam Davidson-Pilon, 2015

illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments.

Real-World Active Learning
Data Mining and Machine Learning

Real-World Active Learning

Ted Cuzzillo, 2015

Applications and Strategies for Human-in-the-loop Machine Learning.

Reinforcement Learning: An Introduction
4.5 (375 Ratings)
Data Mining and Machine Learning

Reinforcement Learning: An Introduction

Richard S. Sutton & Andrew G. Barto, 2012

A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Social Media Mining An Introduction
4.2 (1 Ratings)
Data Mining and Machine Learning

Social Media Mining An Introduction

Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014

Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining

Theory and Applications for Advanced Text Mining
Data Mining and Machine Learning

Theory and Applications for Advanced Text Mining

Shigeaki Sakurai, 2012

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.

Understanding Machine Learning: From Theory to Algorithms
4.3 (75 Ratings)
Data Mining and Machine Learning

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz, 2014

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

An introduction to data science
4.0 (3 Ratings)
Data Science in General

An Introduction to Data Science

Jeffrey Stanton, Syracuse University
Contributions by Robert W. De Graaf

This book was developed for the Certificate of Data Science pro- gram at Syracuse University’s School of Information Studies.

Data Jujitsu: The Art of Turning Data into Product
3.8 (182 Ratings)
Data Science in General

Data Jujitsu: The Art of Turning Data into Product

DJ Patil, 2012
DJ is the "Data Scientist in Residence" at Greylock Partners

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

School of Data Handbook
Data Science in General

School of Data Handbook

School of Data, 2015

The School of Data Handbook is a companion text to the School of Data. Its function is something like a traditional textbook – it will provide the detail and background theory to support the School of Data courses and challenges.

Art of Data Science
3.8 (9 Ratings)
Data Science in General

The Art of Data Science

Roger D. Peng & Elizabeth Matsui, 2015

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience...

D3 Tips and Tricks
Languages: JavaScript
3.9 (8 Ratings)
Data Visualization

D3 Tips and Tricks

Malcolm Maclean, 2015

D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization.

Interactive Data Visualization for the Web
4.1 (418 Ratings)
Data Visualization

Interactive Data Visualization for the Web

Scott Murray, 2013

Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It’s easy and fun with this practical, hands-on introduction.

Data-Intensive Text Processing with MapReduce
4.1 (27 Ratings)
Distributed Computing Tools

Data-Intensive Text Processing with MapReduce

Jimmy Lin & Chris Dyer, 2010

MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google...

Hadoop Illuminated
Distributed Computing Tools

Hadoop Illuminated

Mark Kerzner & Sujee Maniyam, 2014

'Hadoop illuminated' is the open source book about Apache Hadoop™. It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

Hadoop Tutorial as a PDF
Distributed Computing Tools

Hadoop Tutorial as a PDF

Tutorials Point
Online Learning Resource

Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines.

Programming Pig
3.6 (52 Ratings)
Distributed Computing Tools

Programming Pig

Alan Gates, 2011
Alan is a member of the Apache Software Foundation and a co-founder of Hortonworks.

This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop.

Building Data Science Teams
3.7 (297 Ratings)
Forming Data Science Teams

Building Data Science Teams

DJ Patil
DJ is the "Data Scientist in Residence" at Greylock Partners

In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

Data Driven: Creating a Data Culture
3.8 (312 Ratings)
Forming Data Science Teams

Data Driven: Creating a Data Culture

DJ Patil,‎ Hilary Mason
Hilary Mason is the lead scientist at bit.ly, DJ is the "Data Scientist in Residence" at Greylock Partners

In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.

The Data Science Handbook
4.1 (25 Ratings)
Interviews with Data Scientists

The Data Science Handbook

by Carl Shan (Author),‎ William Chen (Author),‎ Henry Wang (Author),‎ Max Song (Author)
25 Data Scientists contributed

The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice.

A Byte of Python
Languages: Python
4.0 (22 Ratings)
Learning Languages

A Byte of Python

Swaroop C H, 2003

‘A Byte of Python’ is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

Advanced R
Languages: R
4.6 (201 Ratings)
Learning Languages

Advanced R

Hadley Wickham, 2014

Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With ten+ years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

A Little Book of R for Time Series
Languages: R
Learning Languages

A Little Book of R for Time Series

Avril Coghlan, 2015

This is a simple introduction to time series analysis using the R statistics software.

Automate the Boring Stuff with Python: Practical Programming for Total Beginners
Languages: Python
4.3 (1444 Ratings)
Learning Languages

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Al Sweigart, 2015

Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.

Dive Into Python 3
Languages: Python
3.9 (254 Ratings)
Learning Languages

Dive Into Python 3

Mark Pilgrim, 2009
Mark Pilgrim is a developer advocate for open source and open standards

This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.

Ecological Models and Data in R
Languages: R
4.2 (33 Ratings)
Learning Languages

Ecological Models and Data in R

Benjamin M. Bolker, 2008

The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Invent with Python
Languages: Python
4.1 (113 Ratings)
Learning Languages

Invent with Python

Albert Sweigart
Albert Sweigart, is a software developer in San Francisco, California

"Invent Your Own Computer Games with Python" teaches you computer programming in the Python programming language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.

Learning Statistics with R
Languages: R
Learning Languages

Learning Statistics with R

Daniel Navarro, 2015

I (Dani) started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form.

Learning with Python 3
Languages: Python
4.1 (14 Ratings)
Learning Languages

Learning with Python 3

Peter Wentworth, Jeffrey Elkner, Allen B. Downey, & Chris Meyers, 2012

Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.

Learn Python, Break Python: A Beginner's Guide to Programming
Languages: Python
4.0 (9 Ratings)
Learning Languages

Learn Python, Break Python

Scott Grant, 2014

This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. After all, everybody has to start somewhere.

Learn Python the Hard Way
Languages: Python
3.9 (132 Ratings)
Learning Languages

Learn Python the Hard Way

Zed A. Shaw, 2013

This is a free sample of Learn Python 2 The Hard Way with 8 exercises and Appendix A available for you to review.

Practical Regression and Anova using R
Languages: R
Learning Languages

Practical Regression and Anova using R

Julian J. Faraway, 2002

This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

python for everybody cover.jpg
Languages: Python
4.3 (259 Ratings)
Learning Languages

Python for Everybody

Dr. Charles R Severance, 2016

This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.

Python for You and Me
Languages: Python
Learning Languages

Python for You and Me

Kushal Das, 2015

This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.

Python Practice Book
Languages: Python
Learning Languages

Python Practice Book

Anand Chitipothu, 2014
Anand conducts Python training classes on a semi-regular basis in Bangalore, India.

This book is prepared from the training notes of Anand Chitipothu.

Python Programming
Languages: Python
Learning Languages

Python Programming

Wikibooks, 2015

This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3.

R by Example
Languages: R
Learning Languages

R by Example

Ajay Shah, 2005
R Programming
Languages: R
Learning Languages

R Programming

Wikibooks, 2014

The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i.e. a book that can be used by all.

R Programming for Data Science
Languages: R
Learning Languages

R Programming for Data Science

Roger D. Peng

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code.

Spatial Epidemiology Notes: Applications and Vignettes in R
Languages: R
Learning Languages

Spatial Epidemiology Notes: Applications and Vignettes in R

Charles DiMaggio, 2014

My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.

The R Inferno
Languages: R
3.9 (7 Ratings)
Learning Languages

The R Inferno

Patrick Burns, 2011

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks.

The R Manuals
Languages: R
Learning Languages

The R Manuals

R Development Core Team

The R Manuals.

Think Python second edition
Languages: Python
4.1 (63 Ratings)
Learning Languages

Think Python 2nd Edition

Allen Downey, 2015
Allen Downey is a Professor of Computer Science at Olin College

This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3.

A First Course in Linear Algebra
3.7 (2 Ratings)
Math Topics

A First Course in Linear Algebra

Robert A Beezer, 2012

This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It has numerous worked examples, exercises and complete proofs, ideal for independent study.

Elementary Applied Topology
4.3 (21 Ratings)
Math Topics

Elementary Applied Topology

Robert Ghrist, 2014

This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.

Elementary Differential Equations
4.6 (5 Ratings)
Math Topics

Elementary Differential Equations

William F. Trench, 2013

This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner.

Introduction to Probability
4.3 (12 Ratings)
Math Topics

Introduction to Probability

Charles M. Grinstead & J. Laurie Snell, 1997

This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.

Linear Algebra
Math Topics

Linear Algebra

David Cherney, Tom Denton & Andrew Waldron, 2013
Linear Algebra: An Introduction to Mathematical Discourse
Math Topics

Linear Algebra: An Introduction to Mathematical Discourse

Wikibooks
Linear Algebra, Theory And Applications
3.5 (1 Ratings)
Math Topics

Linear Algebra, Theory And Applications

Kenneth Kuttler, 2015

This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems

Ordinary Differential Equations
Math Topics

Ordinary Differential Equations

Wikibooks
Probabilistic Models in the Study of Language
Math Topics

Probabilistic Models in the Study of Language

R Levy, 2012
Probability and Statistics Cookbook
Math Topics

Probability and Statistics Cookbook

Matthias Vallentin

The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.

Cassandra Tutorial as a PDF
Languages: Cassandra
SQL, NoSQL, and Databases

Cassandra Tutorial as a PDF

Tutorials Point, 2015
Extracting Data from NoSQL Databases
Languages: NoSQL
SQL, NoSQL, and Databases

Extracting Data from NoSQL Databases

Petter Näsholm, 2012
Graph Databases
Languages: Graph DB
3.6 (18 Ratings)
SQL, NoSQL, and Databases

Graph Databases

Ian Robinson, Jim Webber, & Emil Eifrem, 2013

Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.

NoSQL Databases
Languages: NoSQL
SQL, NoSQL, and Databases

NoSQL Databases

Christof Strauch
SQL for Web Nerds
Languages: SQL
SQL, NoSQL, and Databases

SQL for Web Nerds

Philip Greenspun
SQL Tutorial as a PDF
Languages: SQL
SQL, NoSQL, and Databases

SQL Tutorial as a PDF

Tutorials Point

This tutorial will give you a quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.

The Little MongoDB Book
Languages: MongoDB
SQL, NoSQL, and Databases

The Little MongoDB Book

Karl Seguin, 2011

MongoDB is an open source NoSQL database, easily scalable and high performance. It retains some similarities with relational databases which, in my opinion, makes it a great choice for anyone who is approaching the NoSQL world.

A First Course in Design and Analysis of Experiments
3.1 (11 Ratings)
Statistics

A First Course in Design and Analysis of Experiments

Gary W. Oehlert, 2010

Suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, this book offers a superb balance of both analysis and design.

An Introduction to Statistical Learning with Applications in R
4.6 (1465 Ratings)
Statistics

An Introduction to Statistical Learning with Applications in R

Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Artificial Intelligence: Foundations of Computational Agents
3.5 (17 Ratings)
Statistics

Artificial Intelligence: Foundations of Computational Agents

David Poole & Alan Mackworth, 2010

This is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents.

Intro Stat with Randomization and Simulation
3.7 (6 Ratings)
Statistics

Intro Stat with Randomization and Simulation

David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015

The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and...

OpenIntro Statistics
4.1 (34 Ratings)
Statistics

OpenIntro Statistics

David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015

Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.4 (237 Ratings)
Statistics

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Think Bayes: Bayesian Statistics Made Simple
3.9 (45 Ratings)
Statistics

Think Bayes: Bayesian Statistics Made Simple

Allen B. Downey, 2012

Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (315 Ratings)
Statistics

Think Stats: Exploratory Data Analysis in Python

Allen B. Downey, 2014

This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

Pattern Recognition and Machine Learning book cover
4.3 (1492 Ratings)

Pattern Recognition and Machine Learning

Christopher M. Bishop, 2006

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

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