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

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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

	Starting date	Type	Course duration	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
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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.

Prerequisites

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.

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