Supervised Machine Learning Procedures Using SAS(R) Viya(R) in SAS(R) Studio

Course code: DMML35

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.

and certified lecturers

recognized certifications

Wide range of technical
and soft skills courses

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 14 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 14 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

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

Course structure

Introduction to SAS Viya, Data Preparation, and Exploration

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


  • 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


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.

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