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Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own.

•, Jiawei Han and Micheline Kamber About data mining and data warehousing •, Jure Leskovec, Anand Rajaraman, Jeff Ullman The focus of this book is provide the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases. •, Trevor Hastie, Robert Tibshirani, Jerome Friedman This is a conceptual book in terms of data mining and prediction with a statistical point of view.

Kolodci chertezhi dwg. Covers many machine learning subjects too. •, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Overview of statistical learning based on large datasets of information.

The exploratory techniques of the data are discussed using the R programming language. •, Foster Provost, Tom Fawcett 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. • 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. • On this resource the reality of big data is explored, and its benefits, from the marketing point of view.

It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning. • 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.

• A great resource provided by Wikipedia assembling a lot of machine learning in a simple, yet very useful and complete guide. • A great cover of the data mimning exploratory algorithms and machine learning processes.

These explanations are complemented by some statistical analysis. • 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. • A book about bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language. • A data mining book oriented specifically to marketing and business managent. With great case studies in order to understand how to apply these techniques on the real world.