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

Overview

This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.

Audience

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

Prerequisites

  • Knowledge of your business requirements

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

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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