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.
