Overview
Curriculum
Master the power of ensemble methods and discover how combining models leads to smarter predictions. This module aims to go deep into techniques such as bagging and random forests, explore key concepts like the bias-variance trade-off and out-of-bag evaluation, and build practical skills through interactive notebooks. Whether you’re working with messy real-world data or aiming for stronger model performance, ensemble learning is the next step forward!
What You'll Learn
- Explore key techniques like bagging and random forests in depth
- Apply concepts such as out-of-bag evaluation and binomial probability
- Understand how ensemble methods balance bias and variance
- Gain hands-on experience through interactive, real-world exercises
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