Applied Machine Learning: Ensemble Learning
With Matt Harrison
Liked by 53 users
Duration: 1h 28m
Skill level: Intermediate
Released: 2/28/2025
Course details
Do you want to grow your skills as a machine learning practitioner, but don’t know where to begin? You don’t need any formal training in data science to start working toward your goal. In this course, instructor Matt Harrison guides you through the key concepts of ensemble learning. Explore different ensemble methods like bagging, boosting, and stacking and learn to implement them using popular Python libraries such as scikit-learn and XGBoost. By the end of this course, you’ll be equipped with the skills you need to implement and optimize ensemble models in real-world machine learning tasks.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.
Skills you’ll gain
Earn a sharable certificate
Share what you’ve learned, and be a standout professional in your desired industry with a certificate showcasing your knowledge gained from the course.
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Showcase on your LinkedIn profile under “Licenses and Certificate” section
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Download or print out as PDF to share with others
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Share as image online to demonstrate your skill
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- Learn on the go Access on tablet and phone
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