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Machine Learning with SAS Viya

De (autor): Sas Institute Inc

Coperta cărții 'Machine Learning with SAS Viya - Sas Institute Inc'
Machine Learning with SAS Viya

De (autor): Sas Institute Inc


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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267.47Lei

267.47Lei

297.19 Lei

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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
Citește mai mult

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