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

Audience

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

Prerequisites

• Knowledge of your business requirements

Course topics

1. 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

2. Supervised models: Decision trees – CHAID

• CHAID basics for categorical targets
• Include categorical and continuous predictors
• CHAID basics for continuous targets
• Treatment of missing values

3. 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

4. Evaluation measures for supervised models

• Evaluation measures for categorical targets
• Evaluation measures for continuous targets

5. Supervised models: Statistical models for continuous targets – Linear regression

• Linear regression basics
• Include categorical predictors
• Treatment of missing values

6. Supervised models: Statistical models for categorical targets – Logistic regression

• Logistic regression basics
• Include categorical predictors
• Treatment of missing values

7. Supervised models: Black box models – Neural networks

• Neural network basics
• Include categorical and continuous predictors
• Treatment of missing values

8. Supervised models: Black box models – Ensemble models

• Ensemble models basics
• Improve accuracy and generalizability by boosting and bagging
• Ensemble the best models

9. 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

10. Unsupervised models: TwoStep and Anomaly detection

• TwoStep basics
• TwoStep assumptions
• Find the best segmentation model automatically
• Anomaly detection basics
• Treatment of missing values

11. Association models: Apriori

• Apriori basics
• Evaluation measures
• Treatment of missing values

12. Association models: Sequence detection

• Sequence detection basics
• Treatment of missing values

13. Preparing data for modeling

• Examine the quality of the data
• Select important predictors
• Balance the data