Overview
Curriculum
Boosting is a powerful ensemble technique that builds better models by learning from previous mistakes, one step at a time. In this course, we’ll explore the core ideas behind boosting, explore popular methods like AdaBoost, and apply them through real-world examples and hands-on exercises. Whether you’re just starting out or looking to sharpen your skills, this course will help you create smarter and more accurate predictive models.
What You'll Learn
- Understand the concept of using weak classifiers to create a strong one
- Analyze boosting and its ability to adjust the weight of each classifier
- Apply the logic behind the boosting algorithm to real-world examples
- Evaluate the differences between bagging and boosting
- Discuss the potential drawbacks of the boosting algorithm
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