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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...
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$1,790 USD
Duration
2 days
Course Code
0A079G
Available Formats
Classroom, Virtual
Course Eligible for IBM Digital Badge
This course is available in other formats.
Self-Paced
Self-Paced
Overview
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
- 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
Schedule
1 options available
-
May 13, 2026 - May 14, 2026 (2 days)
Live Virtual | 9:30AM – 5:30PM EDT Language English Select from 1 options below Live Virtual |9:30AM – 5:30PM EDT- Enroll
- Enroll Add to quote
Winter Promotion: Save on this course today.
Promo Code: FROSTBYTE
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.
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At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
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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.
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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?
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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.
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Reviews
Simply great training provider that I can go for updating/acquiring my skill sets.
Course was great and the instructor had an answer for anything that was asked during the course.
Great class I learned a great deal from the material. There would seem to a large amount that I need to learn about.
Very good couse and again we would like to see more videos on removing FRUs
Course was thorough and gave a very solid grounding in the tools advertised.
