Free Data Mining Books

Here is a great list of 20 online books on data mining, machine learning, predictive analytics, and Big Data in various formats available for free:

1. An Introduction to Statistical Learning: with Applications in R: Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
2. Modeling With Data: This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information.
3. Machine Learning – Wikipedia Guide: A great resource provided by Wikipedia assembling a lot of machine learning in a simple, yet very useful and complete guide.
4. Data Mining and Analysis: Fundamental Concepts and Algorithms: A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
5. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More: The exploration of social web data is explained on this book. Data capture from the social media apps, it’s manipulation and the final visualization tools are the focus of this resource.
6. Probabilistic Programming & Bayesian Methods for Hackers: A book about bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language.
7. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management: A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.
8. Inductive Logic Programming Techniques and Applications: An old book about inductive logic programming with great theoretical and practical information, referencing some important tools.
9. An Introduction to Data Science: An introductory level resource developed by a american university that presents a overview of the most important data science’s notions.
10. Mining of Massive Datasets: The main focus of this book is to provide the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases.
11. A Programmer’s Guide to Data Mining: A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.
12. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery: The objective of this book is to provide you lots of information on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
13. Machine Learning, Neural and Statistical Classification: A good old book about statistical methodology, learning techniques and another important issues related to machine learning.
14. Information Theory, Inference, and Learning Algorithms: An interesting approach to information theory merged with the inference and learning concepts. This book taughts a lot of data mining techniques creating a bridge between it and information theory.
15. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die [Broken Link]: A great predictive analytics book providing an insight about the concept, alongside with case studies to consolidate the theory.
16. Gaussian Processes for Machine Learning: This is a theoretical book approaching learning algortihms based on probabilistic gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.
17. Introduction to Machine Learning: A simple, yet very important book, to introduce everyone to the machine learning subject.
18. Machine Learning: A very complete book about the machine learning subject approching several specific, and very useful techniques.
19. Think Bayes, Bayesian Statistics Made Simple: A Python programming language approach to the bayesian statistical methods, where these techniques are applied to solve real-world problems and simulations.
20. Bayesian Reasoning and Machine Learning: Another bayesian book reference, this one focusing on applying it to machine learning algorithms and processes. It is a hands-on resource, great to absorb all the knowledge in the book.