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

Matlab Machine Learning Recipes A Problemsolution Approach 2nd Edition Michael Paluszek

  • SKU: BELL-7416570
Matlab Machine Learning Recipes A Problemsolution Approach 2nd Edition Michael Paluszek
$ 31.00 $ 45.00 (-31%)

4.4

92 reviews

Matlab Machine Learning Recipes A Problemsolution Approach 2nd Edition Michael Paluszek instant download after payment.

Publisher: Apress
File Extension: PDF
File size: 13.88 MB
Pages: 358
Author: Michael Paluszek, Stephanie Thomas
ISBN: 9781484239155, 9781484239162, 1484239156, 1484239164
Language: English
Year: 2018
Edition: 2

Product desciption

Matlab Machine Learning Recipes A Problemsolution Approach 2nd Edition Michael Paluszek by Michael Paluszek, Stephanie Thomas 9781484239155, 9781484239162, 1484239156, 1484239164 instant download after payment.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
What you'll learn:
How to write code for machine learning, adaptive control and estimation using MATLAB

How these three areas complement each other

How these three areas are needed for robust machine learning applications

How to use MATLAB graphics and visualization tools for machine learning

How to code real world examples in MATLAB for major applications of machine learning in big data
Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.  

Related Products