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

Introduction To Algorithms For Data Mining And Machine Learning Xinshe Yang

  • SKU: BELL-10416356
Introduction To Algorithms For Data Mining And Machine Learning Xinshe Yang
$ 31.00 $ 45.00 (-31%)

5.0

88 reviews

Introduction To Algorithms For Data Mining And Machine Learning Xinshe Yang instant download after payment.

Publisher: Academic Press
File Extension: PDF
File size: 2.18 MB
Pages: 173
Author: Xin-She Yang
ISBN: 9780128172162, 9780128172179, 0128172169, 0128172177
Language: English
Year: 2019

Product desciption

Introduction To Algorithms For Data Mining And Machine Learning Xinshe Yang by Xin-she Yang 9780128172162, 9780128172179, 0128172169, 0128172177 instant download after payment.

'Introduction to Algorithms for Data Mining and Machine Learning' introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

Related Products