Machine Learning with SAS Viya
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Machine Learning with SAS Viya

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ISBN-13:
9781951685317
Veröffentl:
2020
Einband:
PDF
Seiten:
386
Autor:
SAS Institute Inc.
eBook Typ:
PDF
eBook Format:
PDF
Kopierschutz:
Adobe DRM [Hard-DRM]
Sprache:
Englisch
Beschreibung:

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 learningData preparation and dealing with missing and unstructured dataModel building and selectionImproving and optimizing modelsModel deployment and monitoring performance
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 learningData preparation and dealing with missing and unstructured dataModel building and selectionImproving and optimizing modelsModel deployment and monitoring performance

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