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Machine Learning Using SAS Viya

SAS via Coursera

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Overview

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This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.

This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.

Syllabus

  1. Course Overview
  2. Getting Started with Machine Learning and SAS® Viya®
  3. Data Preparation and Algorithm Selection
  4. Decision Trees and Ensembles of Trees
  5. Neural Networks
  6. Support Vector Machines
  7. Model Deployment

Taught by

Jeff Thompson and Catherine Truxillo

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