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

  • Tuition USD $850 GSA  $727.96
  • Reviews star_rate star_rate star_rate star_rate star_half 1958 Ratings
  • Course Code 0A039G
  • Duration 1 day
  • Available Formats Classroom, Virtual
0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course Eligible for IBM Digital Badge

This course presents advanced models available in IBM SPSS Modeler. The participant 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.

Skills Gained

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

Who Can Benefit

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

Prerequisites

  • Knowledge of your business requirements
  • Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
  • Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.

Course Details

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

When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals, and private on sites. View our group training page for more information.

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it was good and very informative. Instructure covered everything in detail.

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  • Mar 17, 2021 Mar 17, 2021 (1 day)
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