Just Enough R!: An Interactive Approach to Machine Learning and Analytics, 1st Edition (Paperback) book cover

Just Enough R!

An Interactive Approach to Machine Learning and Analytics, 1st Edition

By Richard J. Roiger

Chapman and Hall/CRC

264 pages | 72 B/W Illus.

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Paperback: 9780367439149
pub: 2020-06-15
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Hardback: 9780367443207
pub: 2020-06-15
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Description

Just Enough R! An Interactive Approach to Machine Learning and Analytics: presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called “seeing then doing” as it first gives step by step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided allowing the reader to execute the scripts as they study the explanations given in the text.

Features

  • Gets you quickly using R as a problem solving tool.
  • Uses RStudio’s integrated development environment.
  • Shows how to interface R with SQLite.
  • Includes examples using R’s Rattle Graphical User Interface.
  • Requires no prior knowledge of R, machine learning, or computer programming.
  • Offers over 50 Scripts written in R. Several of the scripts are problem solving templates that with slight modification, can be used again and again.
  • Covers the most popular machine learning techniques including ensemble-based methods and logistic regression.
  • Includes end of chapter exercises many of which can be solved by modifying existing scripts.
  • Includes datasets from several areas including business, health and medicine, and science.

About the Author

Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for over 30 years.

Table of Contents

1. Introduction to Machine Learning

2. Introduction to R

3. Data Structures & Manipulation

4. Preparing the Data

5. Supervised Statistical Techniques

6. Tree-Based Methods

7. Rule-Based Methods

8. Neural Networks

9. Formal Evaluation Techniques

10. Support Vector Machines

11. Unsupervised Clustering Techniques

12. A Case Study in Predicting Treatment Outcome

About the Author

Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roiger’s Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM004000
COMPUTERS / Intelligence (AI) & Semantics
COM012040
COMPUTERS / Programming / Games
COM014000
COMPUTERS / Computer Science
COM018000
COMPUTERS / Data Processing
COM021000
COMPUTERS / Database Management / General
COM021030
COMPUTERS / Database Management / Data Mining
COM037000
COMPUTERS / Machine Theory
COM042000
COMPUTERS / Natural Language Processing
COM044000
COMPUTERS / Neural Networks
COM051000
COMPUTERS / Programming / General
COM051300
COMPUTERS / Programming / Algorithms
COM062000
COMPUTERS / Data Modeling & Design
COM077000
COMPUTERS / Mathematical & Statistical Software