IBM 0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

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