Books
The best books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.
4.6
(881 Ratings)
An Introduction to Statistical Learning with Applications in R
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.
4.1
(464 Ratings)
Artificial Intelligence A Modern Approach, 1st Edition
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.
4.1
(410 Ratings)
Interactive Data Visualization for the Web
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.
4.5
(310 Ratings)
Information Theory, Inference, and Learning Algorithms
"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
4.2
(290 Ratings)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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.
4.4
(232 Ratings)
Reinforcement Learning: An Introduction
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.
4.0
(219 Ratings)
Data Mining: Practical Machine Learning Tools and Techniques
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.
3.6
(214 Ratings)
Think Stats: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
3.9
(139 Ratings)
Bayesian Reasoning and Machine Learning
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.
4.5
(93 Ratings)
OpenIntro Statistics
Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.