Kurs-ID: IBMe_0A079G
Kurs Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) (0A079G)
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.
Seminarinhalte
Introduction to machine learning models • Taxonomy of machine learning models • Identify measurement levels • Taxonomy of supervised models • Build and apply models in IBM SPSS Modeler
Supervised models: Decision trees - CHAID • CHAID basics for categorical targets • Include categorical and continuous predictors • CHAID basics for continuous targets • Treatment of missing values
Supervised models: Decision trees - C&R Tree
• C&R Tree basics for categorical targets • Include categorical and continuous predictors • C&R Tree basics for continuous targets • Treatment of missing values
Evaluation measures for supervised models • Evaluation measures for categorical targets • Evaluation measures for continuous targets
Supervised models: Statistical models for continuous targets - Linear regression • Linear regression basics • Include categorical predictors • Treatment of missing values
Supervised models: Statistical models for categorical targets - Logistic regression • Logistic regression basics • Include categorical predictors • Treatment of missing values
Association models: Sequence detection • Sequence detection basics • Treatment of missing values
Supervised models: Black box models - Neural networks • Neural network basics • Include categorical and continuous predictors • Treatment of missing values
Supervised models: Black box models - Ensemble models • Ensemble models basics • Improve accuracy and generalizability by boosting and bagging • Ensemble the best models
Unsupervised models: K-Means and Kohonen • 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 values
Preparing data for modeling • Examine the quality of the data • Select important predictors • Balance the data
Zielgruppe
- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Voraussetzungen
- Knowledge of your business requirements
Termine
Hinweis
Das Training findet auf Deutsch statt.