Machine learning with SAS Viya.

Format:

Book

Author/Creator:

SAS Institute Inc., author.

Language:

English

Subjects (All):

SAS (Computer file).

Machine learning.

Computer algorithms.

Algorithms.

algorithms.

Medical Subjects:

Algorithms.

Physical Description:

1 online resource (xx, 364 pages)

Edition:

1st edition

Place of Publication:

Cary, North Carolina : SAS Institute, [2020]

System Details:

text file

Summary:

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

Contents:

Introduction to machine learning

Preparing your data: introduction

Preparing your data: missing and unstructured data

Preparing your data: extract features

Discovery: selecting an algorithm

Decision trees: introduction

Decision trees: improving the model

Decision trees: ensemble and forests

Neural networks: introduction and model architecture

Neural networks: optimizing the model and learning

Support vector machines

Model assessments and deployment

Additional model manager tools and open-source code.

Notes:

Description based on print version record.

Includes bibliographical references.

ISBN:

9781951685317

1951685318

9781951685379

1951685377

OCLC:

1201348789