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0E079G - Introduction to Machine Learning Models Using IBM SPSS Modeler v18.2

Overview

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.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Objectives

Please refer to course overview

Audience

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

Content

1. 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

2. Supervised models: Decision trees - CHAID

  • CHAID basics for categorical targets
  • Include categorical and continuous predictors
  • CHAID basics for continuous targets
  • Treatment of missing values

3. 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

4. Evaluation measures for supervised models

  • Evaluation measures for categorical targets
  • Evaluation measures for continuous targets

5. Supervised models: Statistical models for continuous targets - Linear regression

  • Linear regression basics
  • Include categorical predictors
  • Treatment of missing values

6. Supervised models: Statistical models for categorical targets - Logistic regression

  • Logistic regression basics
  • Include categorical predictors
  • Treatment of missing values

7. Supervised models: Black box models - Neural networks

  • Neural network basics
  • Include categorical and continuous predictors
  • Treatment of missing values

8. Supervised models: Black box models - Ensemble models

  • Ensemble models basics
  • Improve accuracy and generalizability by boosting and bagging
  • Ensemble the best models

9. 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

10. Unsupervised models: TwoStep and Anomaly detection

  • TwoStep basics
  • TwoStep assumptions
  • Find the best segmentation model automatically
  • Anomaly detection basics
  • Treatment of missing values

11. Association models: Apriori

  • Apriori basics
  • Evaluation measures
  • Treatment of missing values

12. Association models: Sequence detection

  • Sequence detection basics
  • Treatment of missing values

13. Preparing data for modeling

  • Examine the quality of the data
  • Select important predictors
  • Balance the data

Prerequisites

  • Knowledge of your business requirements

Certification

Schedule

Show Schedule for:




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