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Great Learning via YouTube


This course on ensemble learning focuses on boosting algorithms to help learners understand the concepts thoroughly. By the end of the course, students will be able to grasp the fundamentals of bagging and boosting, understand how boosting algorithms work, and differentiate between various boosting classifiers like AdaBoost, Gradient Boosting, and XGBoost. The course employs a structured approach with modules covering topics such as bias and variance, ensemble methods, model building, and dataset understanding. The intended audience for this course includes individuals interested in machine learning, data science, and enhancing their skills in algorithmic techniques for improved model performance.


Did you know?.
Introduction to Bias and Variance.
Ensemble Methods.
Understanding Datasets.
Model Building for Decision Tree Model.
How Boosting Algorithm works?.
AdaBoost Classifier.
Gradient Boosting Classifier.
XGBoost Classifier.
Bagging vs Boosting.

Taught by

Great Learning


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