This course provides a theoretical foundation for using machine learning capabilities in SAS Viya, 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 SAS Viya 3.5 e-learning version of this course uses the title SAS Visual Data Mining and Machine Learning in SAS Viya: Interactive Machine Learning.

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 machine learning capabilities in SAS Viya for highly interactive, rapid model fitting

Prerequisites

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. You can gain this knowledge by first attending the Statistics You Need to Know for Machine Learning course.SAS Products Covered

SAS Machine Learning

Course Outline

Introduction to Machine Learning Capabilities in SAS Viya

Introductory concepts.Data exploration. SAS Viya; details.Machine Learning AlgorithmsIntroduction. Machine learning.Neural networks. Support vector machines. Bayesian networks.Ensemble Machine Learning AlgorithmsForests. Gradient boosting.Model Assessment and ImplementationModel assessment. Scoring. Integration with Model Studio.Factorization MachinesTypes of recommendation systems. Explaining factorization machines.