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Ensemble learning is a very important part of machine learning that has the capability to devise solutions and applications across multiple domains out there. In ensemble learning, multiple algorithms are put into play by training them to solve the same problem and this will help us to combine the result and get overall better performance. Since machine learning is all about extracting the maximum performance when working on a large amount of data, it becomes very important that you as a learner have a complete understanding of how these concepts work. Keeping exactly that in mind, we here at Great Learning have come up with this course on bagging and boosting to help you understand the concepts completely and to give you a solid foundation of the same.

00:00:00 Introduction
00:04:36 Did you know?
00:08:23 Introduction to Bias and Variance
00:18:31 Ensemble Methods
00:25:58 Boosting
00:28:54 Understanding Datasets
00:36:44 EDA
00:50:25 Model Building for Decision Tree Model
00:58:21 How Boosting Algorithm works?
01:04:11 AdaBoost Classifier
01:18:43 Gradient Boosting Classifier
01:24:33 XGBoost Classifier
01:35:34 Bagging vs Boosting
01:41:48 Summary

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About Great Learning:
- Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more.