![Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]](https://pixhost.icu/avaxhome/57/573a/573a4a185f00474fa4d2fd167d0967b3-8117994149381164024_medium.webp)
Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]
![Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]](https://pixhost.icu/avaxhome/57/573a/573a4a185f00474fa4d2fd167d0967b3-8117994149381164024.webp)
Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 28m | 208 MB
Instructor: Matt Harrison
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 28m | 208 MB
Instructor: Matt Harrison
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.
Learning objectives
- Understand the fundamental concepts of ensemble learning, including bagging, boosting, and stacking, and their practical applications.
- Gain hands-on experience implementing ensemble models such as random forest, AdaBoost, gradient boosting, XGBoost, and stacking using Python libraries.
- Learn how to tune hyperparameters for different ensemble models to optimize predictive performance.
![Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]](https://pixhost.icu/avaxhome/57/573a/573a4a185f00474fa4d2fd167d0967b3-2126320665205165785_medium.webp)
Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]
![Applied Machine Learning: Ensemble Learning [Released: 2/28/2025]](https://pixhost.icu/avaxhome/57/573a/573a4a185f00474fa4d2fd167d0967b3-2126320665205165785.webp)