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More Free Books & Resources on Data Science, Data Mining That You Should Read!
An introductory level resource developed by Syracuse University
Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
A guide through data mining concepts from a programming point of view. It provides several hands-on problems to practice and tests the subjects taught in this online book.
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.
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 kinds of data and algorithms to process it, based on data mining and machine learning.
A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
Another R based book describing all processes and implementations to explore, transform and store information. It also focuses on the concept of Business Analytics.
A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques to the real world.
The objective of this book is to provide you with lots of information on data manipulation. It focuses on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
This is a theoretical book approaching learning algorithms based on probabilistic Gaussian processes. It's about supervised learning problems, describing models and solutions related to machine learning.
An old book about inductive logic programming with great theoretical and practical information, referencing some important tools.
An interesting approach to information theory merged with the inference and learning concepts. This book taught a lot of data mining techniques making the relationship with information theory.
A simple, yet very important book, to introduce everyone to the machine learning subject.
A very complete book about the machine learning subject approaching several specific, and very useful techniques.
A good old book about statistical methodology, learning techniques and other important issues related to machine learning.
A great resource provided by Wikipedia assembling a lot of machine learning in a simple, yet very useful and complete guide.
The focus of this book provides the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases.
This book focuses some processes to solve analytical problems applied to data. In particular, explains you the theory to create tools for exploring big datasets of information.
This book presents you a lot of pattern recognition stuff based on the perspective of the Bayesian network. Many machine learning concepts are approached and exemplified.
A book about Bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language.
This is a conceptual book in terms of data mining and prediction with a statistical point of view. Covers many machine learning subjects too.
A solid approach to the reinforcement learning thematic providing solution methods. It describes also some very important case studies.
A Python programming language approach to the Bayesian statistical methods, where these techniques are applied to solve real-world problems and simulations.