Build practical supervised machine learning skills by working through the kinds of tasks you may see in data science, machine learning, and AI-related roles. In this course, you’ll learn how to turn business problems into clear ML tasks, choose the right modeling approach, and build supervised learning models for classification, regression, forecasting, and tabular prediction problems.

Supervised Machine Learning

Supervised Machine Learning
This course is part of multiple programs.

Instructor: Professionals from the Industry
Access provided by Pega
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Choose supervised ML approaches; Build regression, SVM, and tree models; Tune ensembles for better performance
Skills you'll gain
- Classification And Regression Tree (CART)
- Regression Analysis
- Machine Learning Algorithms
- Statistical Machine Learning
- Logistic Regression
- Machine Learning
- Model Evaluation
- Applied Machine Learning
- Fine-tuning
- Predictive Modeling
- Model Training
- Supervised Learning
- Decision Tree Learning
- Model Optimization
- Machine Learning Methods
- Artificial Intelligence and Machine Learning (AI/ML)
- Random Forest Algorithm
Tools you'll learn
Details to know

Shareable certificate
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Taught in English
Recently updated!
July 2026
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Build your subject-matter expertise
This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

There are 5 modules in this course
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