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

Ensemble Methods For Machine Learning Gautam Kunapuli

  • SKU: BELL-57257278
Ensemble Methods For Machine Learning Gautam Kunapuli
$ 31.00 $ 45.00 (-31%)

5.0

50 reviews

Ensemble Methods For Machine Learning Gautam Kunapuli instant download after payment.

Publisher: Manning Publications Co.
File Extension: PDF
File size: 24.69 MB
Pages: 352
Author: Gautam Kunapuli
Language: English
Year: 2023

Product desciption

Ensemble Methods For Machine Learning Gautam Kunapuli by Gautam Kunapuli instant download after payment.

 This book was never intended to be just a tutorial with step-by-step instructions and
cut-and-paste code (although you can use it that way, too). There are dozens of suchfantastic tutorials on the web, and they can get you going on your data set in an
instant. Instead, I talk about each new method using an immersive approach inspired
by that first machine-learning paper I ever read and refined in college classrooms
during my time as a graduate lecturer.
 I’ve always felt that to understand a technical topic deeply, it helps to strip it down,
take it apart, and try to put it back together again. I adopt the same approach in this
book: we’ll take ensemble methods apart and (re)create them ourselves. We’ll tweak
them and poke them to see how they change. And, in doing so, we’ll see exactly what
makes them tick!
 I hope this book will be helpful in demystifying those technical and algorithmic
details and get you into the ensemble mindset, be it for your class project, Kaggle
competition, or production-quality application.

Related Products