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

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

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

• 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.

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