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

  • Tuition USD $1,300
  • Reviews star_rate star_rate star_rate star_rate star_half 347 Ratings
  • Course Code DMML34
  • Duration 2 days
  • Available Formats Classroom, Virtual

This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.

Skills Gained

  • 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.

Who Can Benefit

  • 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


  • 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.

Course Details

Introduction to SAS Viya, Data Preparation, and Exploration

  • Introduction to SAS Viya.
  • SAS Cloud Analytic Services architecture.
  • SAS Cloud Analytic Services sessions.
  • SAS Cloud Analytic Services libraries.
  • Development environment: SAS Studio.
  • Data preparation and exploration.


  • Concepts of predictive modeling.
  • Introduction to regression.
  • Managing missing values.
  • Selecting regression inputs.
  • Optimizing regression complexity.
  • Interpreting regression models.
  • Transforming inputs.
  • Categorical inputs.
  • Polynomial regressions.

Decision Tree

  • Tree-structure models.
  • Recursive partitioning.
  • Pruning.
  • Ensemble of trees.

Neural Network

  • Introduction.
  • Network architecture.
  • Learning.

Support Vector Machine

  • Large-margin linear classifier.
  • Methods of solution.
  • Nonlinear classifier: kernel trick.

Model Assessment and Scoring

  • Model assessment and comparison.
  • Model deployment.

Factorization Machines (Self-Study)

  • Factorization machines.

How do I enroll?

A comprehensive listing of ExitCertified courses can be found here. You can register directly for the required course/location when you select "register". If you have any questions or prefer to speak with an ExitCertified education consultant directly, please submit your query here. A representative will contact you shortly.

How do I pay for a class?

You can pay at the time of registration using credit card (Mastercard/Visa/American Express) cheque or PO.

What if I have training credits?

ExitCertified honors all savings programs from the partners we work with. ExitCertified also offers training credits across multiple partners through our FLEX Account.

When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.


Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

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4 options found

  • Oct 5, 2020 Oct 8, 2020 (4 days)
    1:00 PM 4:30 PM EST
  • Oct 5, 2020 Oct 6, 2020 (2 days)
    Cary, NC
    9:00 AM 5:00 PM EST
  • Nov 17, 2020 Nov 18, 2020 (2 days)
    12 PM 8:00 PM EST
  • Nov 17, 2020 Nov 18, 2020 (2 days)
    San Francisco, CA
    9:00 AM 5:00 PM PST
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