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

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

Course Details
Code: 0A079G
Tuition (USD): $1,650.00 • Classroom (2 days)
$1,650.00 • Virtual (2 days)

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.

Skills Gained

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

Who Can Benefit

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

Prerequisites

  • Knowledge of your business requirements

Course Details

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

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