Machine Learning Using SAS Viya

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

Course Overview

In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.

Getting Started with Machine Learning and SAS® Viya

In this module, you learn how you can meet today's business challenges with machine learning using SAS® Viya®. You start working on the project that runs throughout the course.

Data Preprocessing and Algorithm Selection

In this module, you learn to explore the data and finish preparing the data for analysis. You also learn some general considerations for selecting an algorithm.

Decision Trees and Ensembles of Trees

In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.

Neural Networks

In this module, you learn to build neural network models.

Support Vector Machines

In this module, you learn to build support vector machine models.

Model Assessment and Deployment

In this module, you learn how to select the model that best meets the requirements of your business challenge and put the model into production. You also learn about managing the model over time.