This 0E039G: Advanced Machine Learning Models Using IBM SPSS Modeler v18.2 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.

Skills Covered

  • Introduction to advanced machine learning models
  • Group fields: Factor Analysis and Principal Component Analysis
  • Predict targets with Nearest Neighbor Analysis
  • Explore advanced supervised models
  • Introduction to Generalized Linear Models
  • Combine supervised models
  • Use external machine learning models
  • Analyze text data

Who Should Attend

  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Course Curriculum


  • Knowledge of your business requirements
  • Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
  • Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.

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