{"id":10609,"date":"2026-08-10T10:21:15","date_gmt":"2026-08-10T10:21:15","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-applied-machine-learning-ensemble-learning-2025-2\/"},"modified":"2026-08-10T10:21:15","modified_gmt":"2026-08-10T10:21:15","slug":"linkedin-applied-machine-learning-ensemble-learning-2025-2","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-applied-machine-learning-ensemble-learning-2025-2\/","title":{"rendered":"LinkedIn \u2013 Applied Machine Learning: Ensemble Learning 2025-2"},"content":{"rendered":"
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Description<\/span><\/h2>\n
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Applied Machine Learning: Ensemble Learning. Designed for those interested in strengthening their machine learning skills. This course covers the fundamental concepts of ensemble learning, with no formal scientific background required, and teaches techniques such as bagging, boosting, and stacking. Guided by instructor Matt Harrison, participants will learn how to implement these techniques with Python libraries such as scikit-learn and XGBoost, and by the end will be able to build efficient models for real-world problems. The course is integrated with GitHub Codespaces to provide a cloud-based development environment without the need for a local setup, allowing for hands-on practice anytime, anywhere. To learn more about this environment, read the \u201cUsing GitHub Codespaces\u201d section.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n