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

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

Association models: Sequence detection
Sequence detection basics
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