Unsupervised Learning with R
- نوع فایل : کتاب
- زبان : انگلیسی
- نویسنده : Erik Rodriguez Pacheco
- چاپ و سال / کشور: 2015
- تعداد صفحه : 192
- شابک / ISBN : 1785887092
Description
The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning.
If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console.
Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques.
By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects.
If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console.
Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques.
By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects.
