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
Detaylari Göster

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

 

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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