Introduction to Machine Learning Models Using IBM SPSS Modeler V18.2 0A079G Training Course | CourseMonster

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

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

• CHAID basics for categorical targets

• Include categorical and continuous predictors

• CHAID basics for continuous targets

Treatment of missing values

• C&R Tree basics for categorical targets

• C&R Tree basics for continuous targets

• 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

• Supervised models: Statistical models for categorical targets - Logistic regression

• Logistic regression basics

Association models: Sequence detection

• Sequence detection basics

Supervised models: Black box models - Neural networks

• Neural network basics

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

• Evaluation measures

Preparing data for modeling

• Examine the quality of the data

• Select important predictors

Balance the data

Useful links: IBM Training | Data and Analytics training at CourseMonster | CourseMonster course page

Additional workplace outcomes: Participants can explain where Introduction to Machine Learning Models Using IBM SPSS Modeler V18.2 0A079G Training Course | CourseMonster fits in a wider IBM skills roadmap, identify related certifications or follow-on courses, and apply the concepts to real project, operations or service delivery scenarios.

Prerequisites

• No formal prerequisites are required unless specified by the vendor for Introduction to Machine Learning Models Using IBM SPSS Modeler V18.2 0A079G
• A basic understanding of the relevant business, technology or project environment is recommended
• Review the official vendor guidance before booking an exam or certification assessment

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

• CHAID basics for categorical targets

• Include categorical and continuous predictors

• CHAID basics for continuous targets

Treatment of missing values

• C&R Tree basics for categorical targets

• C&R Tree basics for continuous targets

• 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

• Supervised models: Statistical models for categorical targets - Logistic regression

• Logistic regression basics

Association models: Sequence detection

• Sequence detection basics

Supervised models: Black box models - Neural networks

• Neural network basics

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

• Evaluation measures

Preparing data for modeling

• Examine the quality of the data

• Select important predictors

Balance the data

Useful links: IBM Training | Data and Analytics training at CourseMonster | CourseMonster course page

Suggested learning path: After this course, compare related options via the links in the overview and the IBM training category.

Exam and certification details

This course may support a vendor exam, digital badge or professional certification depending on the selected delivery option. Delegates should confirm exam inclusion, voucher availability, prerequisites, pass mark and version before booking.

Official vendor training information: IBM Training.