24 Aug

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

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.

 

Details


Course Code: 0A079G

Brand: DS&BA – SPSS

Category: Analytics

Skill Level: Basic

Duration: 16.00H

Modality: CR

 

Audience


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

 

Prerequisites


  • Knowledge of your business requirements

 

Topic


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

 

Objectives


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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