Advanced Machine Learning Using SAS(R) Viya(R)

Course code: ADML35

This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning Using SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models are also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.
1 080 EUR

1 307 EUR including VAT

Selection of dates
Do you have a question?
+420 731 175 867

and certified lecturers

recognized certifications

Wide range of technical
and soft skills courses

Great customer

Making courses
exactly to measure your needs

Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 1 080 EUR


Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request E-learning 21 hours en 1 080 EUR Register
Upon request Upon request 21 hours en 1 800 EUR Register
G Guaranteed course

Didn't find a suitable date?

Write to us about listing an alternative tailor-made date.


Target group

Advanced machine learning modelers who use Model Studio

Course structure

Machine Learning Fundamentals

  • Model Studio review.
  • Classifier performance.
  • Ensemble learning.

Feature Engineering

  • Introduction to feature engineering.
  • Principal component analysis.
  • Singular value decomposition.
  • Robust principal component analysis.
  • Autoencoders.
  • Transforming categorical variables.

Clustering of Variables and Observations

  • Variable clustering.
  • Cluster analysis.

Anomaly Detection

  • Introduction to anomaly detection.
  • Support vector data description.
  • Semi-supervised learning.

External Models in Model Studio

  • Importing SAS Enterprise Miner models.
  • Running SAS/STAT or SAS Enterprise Miner models.
  • Running open-source models.

Machine Learning Automation

  • Automation in SAS Viya.
  • Data preprocessing and feature engineering.
  • Modeling.
  • Automated pipeline creation.
  • Pipeline automation using REST API (self-study).

Tips and Tricks with Model Studio

  • Managing metadata.
  • Working with analysis elements.
  • Using the SAS Code node.
  • Interpreting models with extracted features.
  • Scoring unsupervised learning models.


Before attending this course, it is recommended that you have done the following:
  • Completed the Machine Learning Using SAS Viya course.
  • Obtained some experience with creating and managing SAS data sets, which you can gain from the SAS Programming 1: Essentials course.
  • Acquired some experience building statistical models using SAS Visual Data Mining and Machine Learning software.
  • Do you need advice or a tailor-made course?


    product support

    ComGate payment gateway MasterCard Logo Visa logo