Just Enough R!

Just Enough R!
  • eBook:
    Just Enough R!: An Interactive Approach to Machine Learning and Analytics
  • Author:
    Richard J. Roiger
  • Edition:
    1 edition
  • Categories:
  • Data:
    June 2, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    364 pages
  • Format:

Book 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.
  • 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, including several 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


Chapter 1. Introduction to Machine Learning
Chapter 2. Introduction to R
Chapter 3. Data Structures and Manipulation
Chapter 4. Preparing the Data
Chapter 5. Supervised Statistical Techniques
Chapter 6. Tree-Based Methods
Chapter 7. Rule-Based Techniques
Chapter 8. Neural Networks
Chapter 9. Formal Evaluation Techniques
Chapter 10. Support Vector Machines
Chapter 11. Unsupervised Clustering Techniques
Chapter 12. A Case Study in Predicting Treatment Outcome

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