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

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

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

• Knowledge of your business requirements

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