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IBM GTP Award 2018
0A079G - Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

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

Course Details
Code: 0A079G
Tuition (USD): $1,650.00 • Classroom (2 days)
$1,650.00 • Virtual (2 days)

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

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