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.
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 detailedillegalscripts written in R that apply the algorithms to solve nontrivial problems with real data. Theillegalscript code is provided, allowing the reader to execute theillegalscripts 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 50illegalscripts 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 existingillegalscripts
- 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 and Information Science Department for over 30 years.
|
- Provides a concise overview of R;
- Navigates RStudio interface, data types, I/O operations, basic functions and data structures;
- Gives detailed examples of data mining sessions using the various learning algorithms;
- Covers R´s rattle interface and contain a short section on Relational Databases using SQLite and how to interface SQLite with R
Preface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions.illegalscript Index.
The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis.
The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis.
Über den Autor
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.
Inhaltsverzeichnis
Preface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions.illegalscript Index.
Klappentext
The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis.
