Machine Learning and Data Science : An Introduction to Statistical Learning Methods with R
Language: English
Published by Technics Publications Okt 2015, 2015
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 63.56
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781634620963
- Title
- Machine Learning and Data Science : An Introduction to Statistical Learning Methods with R
- Author
- Daniel Gutierrez
- Publisher
- Technics Publications Okt 2015
- Publication year
- 2015
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1634620968
- ISBN 13
- 9781634620963
- Item weight
- 581 grams
- Dimensions
- 233x187x22 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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