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
