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

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...

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$815 USD GSA  $612.34
Duration 2 days
Course Code 0E079G-SPVC
Available Formats Self Paced

Overview

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. /terms

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 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

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

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 Ascendient Learning deliver group training?

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

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

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