LENGTH: 8 Hours
Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms
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
Introduction to advanced machine learning models• Taxonomy of models• Overview of supervised models• Overview of models to create natural groupingsGroup 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 scoresPredict targets with Nearest Neighbor Analysis• Nearest Neighbor Analysis basics• Key issues in Nearest Neighbor Analysis• Assess model fitExplore advanced supervised models• Support Vector Machines basics• Random Trees basics• XGBoost basicsIntroduction to Generalized Linear Models• Generalized Linear Models• Available distributions• Available link functionsCombine 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 modelingUse external machine learning models• IBM SPSS Modeler Extension nodes• Use external machine learning programs in IBM SPSS ModelerAnalyze text data• Text Mining and Data Science• Text Mining applications• Modeling with text data
31 Mar 2023
Self Paced Training