Machine Learning Essentials: Practical Guide in R

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Machine Learning Essentials: Practical Guide in R

(23 customer reviews)

37.00 29.95

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 data sets, as well as, for building predictive models.

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Description

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.



Version: Français

23 reviews for Machine Learning Essentials: Practical Guide in R

  1. Laurent D. (verified owner)

    The explanations are clear and concise. The code directly usable. Perfect for quickly finding a solution to a particular problem.
    However, there are no links to navigate within the pdf document (for example from the contents or from the index).

  2. david maupin (verified owner)

    Great book with practical examples!

  3. Eko Subagyo (verified owner)

  4. Yuming (verified owner)

    The explanations are clear and concise. a good book!
    However, cant copy text from the PDF into Rstudio or into Word I only get unreadable gibberish. thank author sent me a code~

  5. Hamidou Sy (verified owner)

  6. Andrzej Ptasznik (verified owner)

  7. Federico (verified owner)

  8. Didier Ouedraogo (verified owner)

    I really appreciate this book. The author explain clearly and put the learner to the up level in the mastery of machine learning with R.

  9. Anonymous (verified owner)

    good text. I get a skill to make best models.

  10. Oscar Salas (verified owner)

  11. José de França Bueno (verified owner)

  12. Anonymous (verified owner)

    na

  13. Troy (verified owner)

    This is a nice introduction to machine learning using modern R syntax.

  14. Bitrus (verified owner)

    Well arranged and logical; I learned the essentials within a short space of time. Definitely recommend

  15. Pavel (verified owner)

    Very professional level, very helpfull book

  16. Anonymous (verified owner)

    Great examples on how to use the caret R package. However, tidymodels is usurping caret, so this book will need an update soon. Also a comparison between tidymodels and more mlr3 would be nice.

  17. Ognjen (verified owner)

    So far the best manual of Datanovia!

    • kassambara (store manager)

      Thank you for the positive feedback, highly appreciated

  18. Ioannis M. (verified owner)

    Great reference, the only issue is the PDF does not have a table of contents to redirect as the other documents do.

    • kassambara (store manager)

      Thank you for the feedback, we’ll check and fix this issue

  19. Anonymous (verified owner)

    excellent quick reference

  20. Tsheten (verified owner)

    I purchased the right book for my research. All codes required for my analysis are well-captured in the book with real-life examples. One thing missing in the book is data management steps (like categorizing, generating new variables, etc) before doing real analysis. Otherwise, I am really satisfied for having the book

  21. Andre S. (verified owner)

  22. Etoma Egot (verified owner)

    Easy purchase for me. I highly recommend it

  23. Joaquín Dutour (verified owner)

    Very good book. Ease and Speed in the purchase and acquisition of the product

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