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Unsupervised Learning with R (Anglais) Broché – 3 décembre 2015
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Description du produit
Présentation de l'éditeur
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
Who This Book Is For
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 approach
This 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.
Biographie de l'auteur
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.
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Commentaires client les plus utiles sur Amazon.com
"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.
For better understanding, the author has explained the topics with the help of good examples along with its code and relevant visual outputs. The usage of visual library to explain the concept is really good. Sometimes it feels that the library functions which are used do not have certain parameters justified. You would have to google the definition and co-relate the setting of parameters to completely understand the code.
It would have been better if the author would have provided a brief definition of the mentioned library methods in the appendix. Also the book doesn’t focus on the mathematical aspect much instead the emphasis is on the application to aid in kick-starting implementation. If you are looking for a deep dive explanation, then you need to refer to another book.
Overall the book can be a good companion to those who are starting their way into using R and who do not want to get distracted with unnecessary details and would want to focus on implementation. Few improvements like adding memory maps for helping the reader with the vast variety of methods used, making it inclusive with the reader using case studies, etc. would have made the book much better.