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0A039G – Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

Home / 0A039G – Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

This course presents advanced models available in IBM SPSS Modeler. The student is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

Course Outline:

Course Outline

Introduction to advanced machine learning models
- Taxonomy of models
- Overview of supervised models
- Overview of models to create natural groupings

Group fields:  Factor Analysis and Principal Component Analysis
- Factor Analysis basics
- Principal Components basics
- Assumptions of Factor Analysis
- Key issues in Factor Analysis
- Improve the interpretability
- Factor and component scores

Predict targets with Nearest Neighbor Analysis
- Nearest Neighbor Analysis basics
- Key issues in Nearest Neighbor Analysis
- Assess model fit

Explore advanced supervised models
- Support Vector Machines basics
- Random Trees basics
- XGBoost basics

Introduction to Generalized Linear Models
- Generalized Linear Models
- Available distributions
- Available link functions

Combine supervised models
- Combine models with the Ensemble node
- Identify ensemble methods for categorical targets
- Identify ensemble methods for flag targets
- Identify ensemble methods for continuous targets
- Meta-level modeling

Use external machine learning models
- IBM SPSS Modeler Extension nodes
- Use external machine learning programs in IBM SPSS Modeler

Analyze text data
- Text Mining and Data Science
- Text Mining applications
- Modeling with text data

Course Audience:

Audience: 

  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Prerequisites:

Course Offerings:

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.

  • Course Outline
  • Course Audience
  • Course Offerings
  • Related Courses

Course Outline

Introduction to advanced machine learning models
- Taxonomy of models
- Overview of supervised models
- Overview of models to create natural groupings

Group fields:  Factor Analysis and Principal Component Analysis
- Factor Analysis basics
- Principal Components basics
- Assumptions of Factor Analysis
- Key issues in Factor Analysis
- Improve the interpretability
- Factor and component scores

Predict targets with Nearest Neighbor Analysis
- Nearest Neighbor Analysis basics
- Key issues in Nearest Neighbor Analysis
- Assess model fit

Explore advanced supervised models
- Support Vector Machines basics
- Random Trees basics
- XGBoost basics

Introduction to Generalized Linear Models
- Generalized Linear Models
- Available distributions
- Available link functions

Combine supervised models
- Combine models with the Ensemble node
- Identify ensemble methods for categorical targets
- Identify ensemble methods for flag targets
- Identify ensemble methods for continuous targets
- Meta-level modeling

Use external machine learning models
- IBM SPSS Modeler Extension nodes
- Use external machine learning programs in IBM SPSS Modeler

Analyze text data
- Text Mining and Data Science
- Text Mining applications
- Modeling with text data

Audience: 

  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Prerequisites:

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.

0A069G – IBM SPSS Modeler Foundations (V18.2)

This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.

0A079G – 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 their subscription, predicting property values, segment customers based on usage, and market basket analysis.