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

Course Details
Code: 0E079G-SPVC
Tuition (USD): $650.00 • Self Paced (2 days)
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This IBM Self-Paced Virtual Class (SPVC) includes:
- PDF course guide available to attendee during and after course
- Lab environment where students can work through demonstrations and exercises at their own pace

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.

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

Topics Covered Introduction to IBM SPSS Modeler 
- Introduction to data science 
- Describe the CRISP-DM methodology 
- Introduction to IBM SPSS Modeler 
- Build models and apply them to new data 

Collect initial data 
- Describe field storage 
- Describe field measurement level 
- Import from various data formats 
- Export to various data formats 

Understand the data 
- Audit the data 
- Check for invalid values 
- Take action for invalid values 
- Define blanks 

Set the unit of analysis 
- Remove duplicates 
- Aggregate data 
- Transform nominal fields into flags 
- Restructure data 

Integrate data 
- Append datasets 
- Merge datasets 
- Sample records 

Transform fields 
- Use the Control Language for Expression Manipulation 
- Derive fields 
- Reclassify fields 
- Bin fields 

Further field transformations 
- Use functions 
- Replace field values 
- Transform distributions 

Examine relationships 
- Examine the relationship between two categorical fields 
- Examine the relationship between a categorical and continuous field 
- Examine the relationship between two continuous fields 

Introduction to modeling 
- Describe modeling objectives 
- Create supervised models 
- Create segmentation models 

Improve efficiency 
- Use database scalability by SQL pushback 
- Process outliers and missing values with the Data Audit node 
- Use the Set Globals node 
- Use parameters 
- Use looping and conditional execution

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