Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

Daniel D. Gutierrez

ISBN 10: 1634620968 ISBN 13: 9781634620963

Editorial: Technics Publications LLC, 2015

Idioma: Ingl�s

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Descripci�n

Num Pages: 230 pages. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 235 x 190. . . 2015. First. Paperback. . . . .

N� de ref. del art�culo V9781634620963

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Detalles bibliogr�ficos

T�tulo

Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

Editor

Technics Publications LLC

Fecha de publicaci�n

2015

Idioma

Ingl�s

ISBN 10

1634620968

ISBN 13

9781634620963

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation.

Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.

The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

Acerca del autor

Daniel D. Gutierrez is a practicing data scientist through his Santa Monica, Calif. consulting firm AMULET Analytics. Daniel also serves as Managing Editor for insideBIGDATA.com where he keeps a pulse on this dynamic industry. He is also an educator and teaches classes in data science, machine learning and R for universities and large enterprises. Daniel holds a BS degree in mathematics and computer science from UCLA.

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