This course provides a theoretical foundation for SAS Visual Data Mining and Machine Learning, as well as hands-on experience using the tool through the SAS Visual Analytics interface. The course uses an interactive approach to teach you visualization, model assessment, and model deployment while introducing you to a variety of machine learning techniques.

The latest version of the e-learning course uses the revised title “Interactive Machine Learning in SAS Viya.”

Learn how to

• Train a Bayesian network.
• Train a forest model.
• Train a gradient boosting model.
• Train a neural network.
• Train a support vector machine.
• Train factorization machines.
• Compare models.
• Export model score code and score a model.
• Transfer analytical models from SAS Visual Analytics to Model Studio.

Who should attend

Predictive modelers, business analysts, and data scientists who want to take advantage of SAS Visual Data Mining and Machine Learning for highly interactive, rapid model fitting in SAS Viya

Formats available	Standard duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	14 hours/180 day	System Requirements

Before attending this course, you should:

• Be acquainted with SAS Visual Statistics.
• Have at least an introductory-level familiarity with machine learning techniques and statistical modeling.

This course addresses SAS Visual Data Mining and Machine Learning software.

Introduction to SAS Visual Data Mining and Machine Learning

• Overview.
• Data exploration.
• SAS Viya: details.

Machine Learning Algorithms

• Introduction.
• Neural networks.
• Support vector machines.
• Forests.
• Gradient boosting.
• Bayesian networks.

Model Assessment and Implementation

• Model assessment.
• Scoring.
• Integration with Model Studio.

Factorization Machines

• Different types of recommendation systems.
• Explaining factorization machines.