Boosting

Overview

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Dive into ensemble learning with this comprehensive video tutorial on boosting techniques. Explore the foundations of machine learning, including bias and variance, before delving into ensemble methods. Gain hands-on experience with datasets, perform exploratory data analysis, and build decision tree models. Master various boosting algorithms such as AdaBoost, Gradient Boosting, and XGBoost. Compare bagging and boosting approaches to enhance your understanding of ensemble techniques. By the end of this 1 hour and 44 minute session, acquire a solid foundation in boosting methods and their applications in solving complex machine learning problems across multiple domains.

Syllabus

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