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.

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)

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

Model Assessment and Implementation

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

Factorization Machines

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