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

Technics Publications
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 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.

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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. 

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Additional Information

Publisher
Technics Publications
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Published on
Nov 1, 2015
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Pages
282
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ISBN
9781634620987
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Language
English
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Genres
Computers / Databases / Data Mining
Computers / Machine Theory
Computers / Mathematical & Statistical Software
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Content Protection
This content is DRM protected.
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Available on Android devices
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Gareth James
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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