Machine Learning with SAS Viya
(eBook)

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Published
SAS Institute, 2020.
Format
eBook
Status
Available Online

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Language
English
ISBN
9781951685379

Syndetics Unbound

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Citations

APA Citation, 7th Edition (style guide)

Various Authors., & Various Authors|AUTHOR. (2020). Machine Learning with SAS Viya . SAS Institute.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Various Authors and Various Authors|AUTHOR. 2020. Machine Learning With SAS Viya. SAS Institute.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Various Authors and Various Authors|AUTHOR. Machine Learning With SAS Viya SAS Institute, 2020.

MLA Citation, 9th Edition (style guide)

Various Authors, and Various Authors|AUTHOR. Machine Learning With SAS Viya SAS Institute, 2020.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work IDf15e9f0f-889e-38b1-31b3-becee5264c1b-eng
Full titlemachine learning with sas viya
Authorauthors various
Grouping Categorybook
Last Update2023-08-15 21:00:35PM
Last Indexed2024-04-27 05:41:11AM

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First LoadedMar 1, 2023
Last UsedApr 14, 2024

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    [synopsis] => 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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