This course covers a variety of machine learning techniques that are performed in a scalable and in-memory execution environment. The course provides hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The machine learning techniques include logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, factorization machine, and Bayesian networks.

The self-study e-learning includes:

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

Learn how to

• Create a SAS Cloud Analytic Services (CAS) session, and prepare and explore data for machine learning.
• Build linear and logistic regression models.
• Build decision tree, forest, and gradient boosting models.
• Build neural network models.
• Build support vector machine models.
• Build factorization machine models.
• Evaluate and compare model results.
• Score selected models.
• Build a Bayesian network model.

Who should attend

Data analysts, data miners, mathematicians, statisticians, data scientists, citizen data scientists, qualitative experts, and others who want an introduction to supervised machine learning for predictive modeling

Formats available	Duration
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)
e-Learning:	14 hours/180 day license

Before attending this course, you should have, at minimum, an introductory-level familiarity with basic statistics. SAS experience is helpful but not required. Coding experience is helpful but not required.

This course addresses SAS Viya software.

Introduction to SAS Viya, Data Preparation, and Exploration

• Introduction to machine learning and SAS Viya.
• Supervised machine learning concepts.

Regression

• Introduction to regression.
• Categorical inputs.
• Interactions and polynomials.
• Selecting regression effects.
• Optimizing regression complexity.
• Interpreting regression models.
• Adjustments for oversampling.

Decision Tree

• Tree-structure models.
• Decision tree model essentials.
• Ensemble of trees.

Neural Network

• Introduction to neural networks.
• Neural network modeling essentials.
• Network architecture.
• Network learning.

Model Assessment

• Model assessment and comparison.

Support Vector Machine

• Introduction to support vector machines.
• Methods of solution.

Bayesian Networks

• Introduction.
• Network structures.

Factorization Machines

• Introduction to factorization machines.

Selected Topics