logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

The Art of Machine Learning: A Hands-On Guide to Machine Learning with R Norman Matloff

  • SKU: BELL-53591892
The Art of Machine Learning: A Hands-On Guide to Machine Learning with R Norman Matloff
$ 31.00 $ 45.00 (-31%)

4.8

54 reviews

The Art of Machine Learning: A Hands-On Guide to Machine Learning with R Norman Matloff instant download after payment.

Publisher: No Starch Press, Inc.
File Extension: EPUB
File size: 18.26 MB
Pages: 272
Author: Norman Matloff
ISBN: 9781098168759, 1098168755
Language: English
Year: 2023

Product desciption

The Art of Machine Learning: A Hands-On Guide to Machine Learning with R Norman Matloff by Norman Matloff 9781098168759, 1098168755 instant download after payment.

Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You’ll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbors method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks.Final chapters introduce text and image classification, as well as time series. You’ll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you’ll need in practice. Additional features

How to avoid common problems, such as dealing with “dirty” data and factor variables with large numbers of levels

A look at typical misconceptions, such as dealing with unbalanced data

Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method

Dozens of illustrative examples involving real datasets of varying size and field of application

Standard R packages are used throughout, with a simple wrapper interface to provide convenient access.

After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.

Related Products