Top 27 free Data Mining books for data miners

Published April 1, 2015   |   

Are you looking for some free books to learn about Data Ming, a process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis.? Here is an epic list of absolutely free books on Data Mining.

Free Data Mining books

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. Data Science for Business: What you need to know about data mining and data-analytic thinking
An introduction to data sciences principles and theory, explaining the necessary analytical thinking to approach these kind of problems. It discusses various data mining techniques to explore information.
3. 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.
4. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to store these kind of data and algorithms to process it, based on data mining and machine learning.
5. Data Mining: Practical Machine Learning Tools and Techniques
Full of real world situations where machine learning tools are applied, this is a practical book which provides you the knowledge and hability to master the whole process of machine learning.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. Inductive Logic Programming Techniques and Applications
An old book about inductive logic programming with great theoretical and practical information, referencing some important tools.
12. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
This is a conceptual book in terms of data mining and prediction from a statistical point of view. Covers many machine learning subjects too.
13. 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.
14. 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.
15. 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.
16. 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.
17. Reinforcement Learning: An introduction
A solid approach to the reinforcement learning thematic providing solution methods. It describes also some very important case studies.
18. Pattern Recognition and Machine Learning (Information Science and Statistics)
This book presents you a lot of pattern recognition stuff based on the bayesian networks perspective. Many machine learning concepts are approached and exemplified.
19. Machine Learning, Neural and Statistical Classification
A good old book about statistical methodology, learning techniques and another important issues related to machine learning.
20. 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.
21. 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.
22. Introduction to Machine Learning
A simple, yet very important book, to introduce everyone to the machine learning subject.
23. Data Mining and Business Analytics with R
Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.
24. Machine Learning
A very complete book about the machine learning subject approching several specific, and very useful techniques.
25. 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.
26. 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.
27. 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.

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