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Machine Learning with SAS Viya

Machine Learning with SAS Viya

Machine Learning with SAS Viya Hardback - 2020

by Created by Sas Institute Inc

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decisio
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Details

  • Title Machine Learning with SAS Viya
  • Author Created by Sas Institute Inc
  • Binding Hardback
  • Condition New
  • Pages 386
  • Volumes 1
  • Language ENG
  • Publisher SAS Institute
  • Publication date 2020-05-29
  • Features Bibliography
  • Bookseller's Inventory # ria9781951685393_inp
  • ISBN 9781951685393 / 1951685393
  • Weight 1.93 lbs (0.88 kg)
  • Dimensions 9.25 x 7.5 x 0.88 in (23.50 x 19.05 x 2.24 cm)
  • Category Computers - General Information
  • Library of Congress subjects Algorithms, Computer algorithms
  • Library of Congress Catalogue Number 2022276750
  • Quantity available 446

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Reader reviews for Machine Learning with SAS Viya

From the publisher

Master machine learning with SAS Viya!

Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered - step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills.

In this book, you will learn about:

  • Supervised and unsupervised machine learning
  • Data preparation and dealing with missing and unstructured data
  • Model building and selection
  • Improving and optimizing models
  • Model deployment and monitoring performance
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