About This Book
- Get to grips with the application of Machine Learning methods using an extensive set of R packages
- Understand the benefits and potential pitfalls of using machine learning methods
- Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system
Who This Book Is For
If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.
What You Will Learn
- Gain deep insights to learn the applications of machine learning tools to the industry
- Manipulate data in R efficiently to prepare it for analysis
- Master the skill of recognizing techniques for effective visualization of data
- Understand why and how to create test and training data sets for analysis
- Familiarize yourself with fundamental learning methods such as linear and logistic regression
- Comprehend advanced learning methods such as support vector machines
- Realize why and how to apply unsupervised learning methods
Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R―a cross-platform, zero-cost statistical programming environment―there has never been a better time to start applying machine learning to your data.
The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series.
The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.
Style and approach
This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.
Table of Contents
Chapter 1. A Process for Success
Chapter 2. Linear Regression – The Blocking and Tackling of Machine Learning
Chapter 3. Logistic Regression and Discriminant Analysis
Chapter 4. Advanced Feature Selection in Linear Models
Chapter 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Chapter 6. Classification and Regression Trees
Chapter 7. Neural Networks
Chapter 8. Cluster Analysis
Chapter 9. Principal Components Analysis
Chapter 10. Market Basket Analysis and Recommendation Engines
Chapter 11. Time Series and Causality
Chapter 12. Text Mining
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