When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
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...
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$1,690 USD
GSA
$1,447.36
Course Code
0A079G
Duration
2 days
Available Formats
Classroom, Virtual
Course Eligible for IBM Digital Badge
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.
Skills Gained
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
Who Can Benefit
- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Prerequisites
- Knowledge of your business requirements
Course Details
Course Outline
Introduction to machine learning models- Taxonomy of machine learning models- Identify measurement levels- Taxonomy of supervised models- Build and apply models in IBM SPSS ModelerSupervised models: Decision trees - CHAID- CHAID basics for categorical targets- Include categorical and continuous predictors- CHAID basics for continuous targets- Treatment of missing valuesSupervised 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 valuesEvaluation measures for supervised models- Evaluation measures for categorical targets- Evaluation measures for continuous targetsSupervised models: Statistical models for continuous targets - Linear regression- Linear regression basics- Include categorical predictors- Treatment of missing valuesSupervised models: Statistical models for categorical targets - Logistic regression- Logistic regression basics- Include categorical predictors- Treatment of missing valuesSupervised models: Black box models - Neural networks- Neural network basics- Include categorical and continuous predictors- Treatment of missing valuesSupervised models: Black box models - Ensemble models- Ensemble models basics- Improve accuracy and generalizability by boosting and bagging- Ensemble the best modelsUnsupervised 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 KohonenUnsupervised models: TwoStep and Anomaly detection- TwoStep basics- TwoStep assumptions- Find the best segmentation model automatically- Anomaly detection basics- Treatment of missing valuesAssociation models: Apriori- Apriori basics- Evaluation measures- Treatment of missing valuesAssociation models: Sequence detection- Sequence detection basics- Treatment of missing valuesPreparing data for modeling- Examine the quality of the data- Select important predictors- Balance the data
