Detailed Course Outline
Introduction to advanced machine learning models
- Taxonomy of models
- Overview of supervised models
- Overview of models to create natural groupings
Group fields: Factor Analysis and Principal Component Analysis
- Factor Analysis basics
- Principal Components basics
- Assumptions of Factor Analysis
- Key issues in Factor Analysis
- Improve the interpretability
- Factor and component scores
Predict targets with Nearest Neighbor Analysis
- Nearest Neighbor Analysis basics
- Key issues in Nearest Neighbor Analysis
- Assess model fit
Explore advanced supervised models
- Support Vector Machines basics
- Random Trees basics
- XGBoost basics
Introduction to Generalized Linear Models
- Generalized Linear Models
- Available distributions
- Available link functions
Combine supervised models
- Combine models with the Ensemble node
- Identify ensemble methods for categorical targets
- Identify ensemble methods for flag targets
- Identify ensemble methods for continuous targets
- Meta-level modeling
Use external machine learning models
- IBM SPSS Modeler Extension nodes
- Use external machine learning programs in IBM SPSS Modeler
Analyze text data
- Text Mining and Data Science
- Text Mining applications
- Modeling with text data