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 self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.
  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 license	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.

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

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

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

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