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Course Description

Ensemble modeling is a helpful and important technique used in machine learning. It's a powerful approach to train multiple models and quantify them into a single prediction. There are three commonly used ensemble techniques: stacking, bagging, and boosting. So how do you know which ensemble method to use and when to use it?

In this course, you will explore stacking, bagging, and boosting techniques, including the motivation behind using each and understanding their optimal scenarios as well as their tradeoffs. By the end of this course, you will have observed a number of robust algorithm case studies, such as random forests and gradient boosted decision trees, that employ these methods. You will also have the opportunity to put this new knowledge into action by practicing building and optimizing various ensemble models.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Machine Learning Foundations
  • Managing Data in Machine Learning
  • Training Common Machine Learning Models
  • Training Linear Models
  • Evaluating and Improving Your Mode

Faculty Author

Brian D'Alessandro

Benefits to the Learner

  • Explore the bias-variance tradeoff
  • Improve model performance with ensemble methods
  • Understand the mechanics of random forests and gradient boosted decision trees
  • Identify differences among ensemble methods and when to use them
  • Navigate design decisions and constraints to perform agile model development
  • Build and tune different models to see how various methods can improve models

Target Audience

  • Data scientists and data analysts
  • Programmers, developers, and software engineers
  • Statisticians
  • Product managers
  • Entrepreneurs
  • Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
Jun 19, 2024 to Jul 02, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Sep 11, 2024 to Sep 24, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Dec 04, 2024 to Dec 17, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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