SAS(R) AI & Machine Learning Certification Program

Course Details
Code: AIBT13
Tuition (USD): $2,920.00 • Classroom (14 days)
The SAS AI & Machine Learning Certification Program is designed for data professionals who want to learn artificial intelligence and machine learning techniques using SAS Viya. Students learn to train and deploy supervised machine learning models, classify image data and analyze text with deep learning, uncover hidden insights within unstructured data, develop and score time series forecasting models, and perform several useful optimization techniques in SAS Viya.

The coursework and preparation sessions help prepare candidates for the three exams needed to earn a SAS Certified Professional: Artificial Intelligence and Machine Learning credential.

This course can help prepare you for the following certification exams: SAS Certified Specialist: Machine Learning Using SAS Viya 3.4, SAS Certified Specialist: Natural Language and Computer Vision, SAS Certified Specialist: Forecasting and Optimization.

Skills Gained

  • Apply the analytical life cycle to business need.
  • Prepare and explore data for analytical model development.
  • Train models using deep learning techniques.
  • Import and transform image data for analysis.
  • Explore collections of text documents to discover key insights.
  • Build and share custom pipelines for large-scale forecasting analyses.
  • Refine forecast models to improve forecast accuracy.
  • Solve optimization problems commonly encountered in industry.
  • Identify and formulate appropriate approaches to solving optimization problems.

Who Can Benefit

  • Programmers, business analysts, or statisticians with experience using SAS Viya and writing programs in SAS or other programming languages


  • It is recommended that you have prior programing experience using SAS, Python, or R. You can gain programming knowledge in the SAS(R) Programming I: Essentials course. Candidates should also have some experience with the visual interfaces in SAS Viya. You can learn about those interfaces in the &svs or &yva1 course. It is helpful to have conceptual understanding of regression models, which you can gain by completing the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course. It is also helpful to have conceptual understanding of neural network models, which you can gain by completing the Introduction to Neural Networks in SAS(R) or Neural Network Modeling course.

Course Details

The comprehensive curriculum includes the following courses:

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