Schulung - IBM 0A079G - Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course Outline

• 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

Objective

• 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