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Unsupervised Learning with R, by Erik Rodriguez Pacheco
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Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
About This Book- Unlock and discover how to tackle clusters of raw data through practical examples in R
- Explore your data and create your own models from scratch
- Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide
This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement.
What You Will Learn- Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization
- Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data
- Build and interpret clustering models using K-Means algorithms in R
- Build and interpret clustering models by Hierarchical Clustering Algorithm's in R
- Understand and apply dimensionality reduction techniques
- Create and use learning association rules models, such as recommendation algorithms
- Use and learn about the techniques of feature selection
- Install and use end-user tools as an alternative to programming directly in the R console
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.
Style and approachThis book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.
- Sales Rank: #936201 in Books
- Published on: 2015-12-03
- Released on: 2015-12-03
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .44" w x 7.50" l, .75 pounds
- Binding: Paperback
- 192 pages
About the Author
Erik Rodriguez Pacheco
Erik Rodriguez Pacheco works as a manager in the business intelligence unit at Banco Improsa in San Jose, Costa Rica, where he holds 11 years of experience in the financial industry. He is currently a professor of the business intelligence specialization program at the Instituto Tecnologico de Costa Rica's continuing education programs. Erik is an enthusiast of new technologies, particularly those related to business intelligence, data mining, and data science. He holds a bachelor's degree in business administration from Universidad de Costa Rica, a specialization in business intelligence from the Instituto Tecnologico de Costa Rica, a specialization in data mining from Promidat (Programa Iberoamericano de Formacion en Mineria de Datos), and a specialization in business intelligence and data mining from Universidad del Bosque, Colombia. He is currently enrolled in an online specialization program in data science from Johns Hopkins University. He has served as the technical reviewer of R Data Visualization Cookbook and Data Manipulation with R - Second Edition, both from Packt Publishing. He can be reached at https://www.linkedin.com/in/erikrodriguezp.
Most helpful customer reviews
2 of 3 people found the following review helpful.
Low quality, low value-added
By Dimitri Shvorob
Packt's conveyor is not slowing down: only three months ago I surveyed their fresh crop of "data science with R" offerings
"Mastering Predictive Analytics with R" by Forte, �32.99
"Mastering Machine Learning with R" by Lesmeister, �34.99
"R Data Analysis Cookbook" by Viswanathan and Viswanathan, �29.99
"Machine Learning with R Cookbook" by Yu-Wei, �30.99
and now there are four more:
"Unsupervised Learning with R" by Pacheco, �25.99
"Data Analysis with R" by Fischetti, �34.99
"Learning Predictive Analytics with R" by Mayor, �31.99
"Mastering Data Analysis with R" by Daroczi, �34.99
So far I have gone through the first two "new" titles. Fischetti's is the rare exception from the norm, the good book in Packt's sea of dross. (It is, however, much closer to "proper" statistics than to machine-learning methods, so - skipping ahead - not a direct competitor to this title).
Pacheco's, on the other hand, is a typical Packt product, a glorified copy-paste of vignettes of several R packages, with minimal effort to integrate and present the content, or explain the algorithms. The book's "unique selling proposition", it seems, is covering one or two relevant packages more per topic than its competitors do. On the other hand, those competitors - take Forte, for example - cover a lot of topics when "Unsupervised Learning" attempts only three: clustering, association rules, and PCA.
Pass this inferior product without a second thought. In the Packt stable, better options are the books by Forte and Lantz. The best book overall is "Introduction to Statistical Learning" by Witten et al.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
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