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. Terms and Conditions: Ingram Micro – https://www.ingrammicrotraining.com/Terms-of-use.aspx; IBM – http://www.ibm.com/training/terms

Version:

v18.2

Audience:

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

Objectives:

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

Detail:

alues 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

Pre-Requisites:

Knowledge of your business requirements