Machine Learning Essentials: Practical Guide in R

Alboukadel Kassambara

Published by CreateSpace Independent Publishing Platform, 2018
ISBN 10: 1986406857 / ISBN 13: 9781986406857
Used / Paperback / Quantity Available: 0
From Books Express (Kittery, ME, U.S.A.)
Available From More Booksellers
View all  copies of this book

About the Book

We're sorry; this specific copy is no longer available. AbeBooks has millions of books. We've listed similar copies below.


Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!. Bookseller Inventory # 1986406857

About this title:

Synopsis: Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.

This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.

The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

  • Covers machine learning algorithm and implementation
  • Key mathematical concepts are presented
  • Short, self-contained chapters with practical examples.

"About this title" may belong to another edition of this title.

Bibliographic Details

Title: Machine Learning Essentials: Practical Guide...
Publisher: CreateSpace Independent Publishing Platform
Publication Date: 2018
Binding: Paperback
Book Condition: Good
Edition: 1.

Top Search Results from the AbeBooks Marketplace


Kassambara, Alboukadel
Published by CreateSpace Independent Publishing Platform (2018)
ISBN 10: 1986406857 ISBN 13: 9781986406857
New Paperback Quantity Available: 1
Print on Demand
Revaluation Books
(Exeter, United Kingdom)

Book Description CreateSpace Independent Publishing Platform, 2018. Paperback. Condition: Brand New. 1st edition. 210 pages. 10.00x8.00x0.50 inches. This item is printed on demand. Seller Inventory # zk1986406857

More information about this seller | Contact this seller

Buy New
US$ 102.09
Convert currency

Add to Basket

Shipping: US$ 10.00
From United Kingdom to U.S.A.
Destination, rates & speeds