- Introduction to machine learning models
- Supervised models: Decision trees – CHAID
- Supervised models: Decision trees – C&R Tree
- Evaluation measures for supervised models
- Supervised models: Statistical models for continuous targets – Linear regression
- Supervised models: Statistical models for categorical targets – Logistic regression
- Supervised models: Black box models – Neural networks
- Supervised models: Black box models – Ensemble models
- Unsupervised models: K-Means and Kohonen
- Unsupervised models: TwoStep and Anomaly detection
- Association models: Apriori
- Association models: Sequence detection
- Preparing data for modeling
Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) Seminar
Introduction to supervised - , unsupervised -, and association models.
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.
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Inhalte IBM SPSS Machine Learning Seminar
Zielgruppe IBM SPSS Machine Learning Seminar
- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Voraussetzungen IBM SPSS Machine Learning Seminar
- Knowledge of your business requirements