This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.
Knowledge of your business requirements
Data scientistsBusiness analystsClients who want to learn about machine learning models
Introduction to machine learning modelsTaxonomy of machine learning modelsIdentify measurement levelsTaxonomy of supervised modelsBuild and apply models in IBM SPSS Modeler� Supervised models: Decision trees - CHAIDCHAID basics for categorical targetsInclude categorical and continuous predictorsCHAID basics for continuous targetsTreatment of missing values� Supervised models: Decision trees - C K-Means basics, Include categorical inputs in K-Means, Treatment of missing values in K-Means, Kohonen networks basics, Treatment of missing values in Kohonen, Unsupervised models: TwoStep and Anomaly detection, TwoStep basics, TwoStep assumptions, Find the best segmentation model automatically, Anomaly detection basics, Treatment of missing values, Association models: Apriori, Apriori basics, Evaluation measures, Treatment of missing valuesPreparing data for modeling, Examine the quality of the data, Select important predictors, Balance the dataCloud Data and AI, Information Architecture Market, AI Tools Decision Optimization