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

Machine Learning In Action 1st Edition Peter Harrington

  • SKU: BELL-54973138
Machine Learning In Action 1st Edition Peter Harrington
$ 31.00 $ 45.00 (-31%)

5.0

80 reviews

Machine Learning In Action 1st Edition Peter Harrington instant download after payment.

Publisher: Manning Publications
File Extension: PDF
File size: 8.59 MB
Pages: 383
Author: Peter Harrington
Language: English
Year: 2012
Edition: 1

Product desciption

Machine Learning In Action 1st Edition Peter Harrington by Peter Harrington instant download after payment.

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.
 
Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.
 
Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

Related Products