Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

• Data scientists
• Business analysts
• Clients who want to learn about machine learning models

At the end of the course, participants will be able to :

• Use machine learning models
• Prepare data for modeling

• 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

• Knowledge of your business requirements
• Basic understanding of Data Science