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This module explores ensemble methods, bagging, and random forests in machine learning, emphasizing their significance in enhancing model performance and reliability. Participants delve into concepts such as Bootstrap sampling and Binomial distribution, understanding how they contribute to creating robust models. By mastering these techniques, learners gain the ability to optimize model performance, mitigate overfitting, and achieve better results in various applications like image recognition and predictive modeling. The module also covers the Boosting algorithm, enabling participants to analyze its nuances, apply it to real-world scenarios, and evaluate its drawbacks compared to Bagging techniques.
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Free
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8 lessons
English
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