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

Course overview

This course teaches how to use machine learning models to predict categorical and continuous targets, to create natural groupings, and to find associations.

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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

Who should attend

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

Prerequisites

• Knowledge of your business requirements

Delivery formats

• • Instructor-led online (virtual classroom)
• • Instructor-led on-site at your premises
• • Self-paced online training
• • Private/closed group delivery for corporate teams

Related and follow-on courses

• 0G51BG — Statistical Analysis Using IBM SPSS Statistics (V26) (Intermediate)
• 0A069G — IBM SPSS Modeler Foundations (V18.2) (Basic)
• 0G53BG — IBM SPSS Statistics Essentials (V26) (Basic)
• 2LA22G — IBM Db2 12: SQL Workshop (Basic)