Kurs-ID: IBM_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.
Alle IBM Trainings werden mit Original IBM Schulungsunterlagen angeboten und finden in Kooperation mit dem von Arrow ECS autorisierten IBM Schulungspartner esciris statt.
Anfrage stellenSeminarinhalte
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.