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Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) – 0a079gpl

Course #: 0A079GPL

Duration: 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.

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

Audience

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

Prerequisites

  • Knowledge of your business requirements

Topics

  • 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

Contact us regarding the training