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

Gutierrez, Daniel D.

ISBN 10: 1634620968 / ISBN 13: 9781634620963

Published by Technics Publications, 2015

About this Item

Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.

Seller Inventory # 4J3P7J0029H3

Bibliographic Details

• Title: Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R
• Publisher: Technics Publications
• Publication Date: 2015
• Language: English
• ISBN 10: 1634620968
• ISBN 13: 9781634620963
• Binding: Soft cover
• Condition: acceptable

About this title

Synopsis

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.

About the Author

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.