BAGGING vs BOOSTING vs STACKING in Ensemble Learning Machine Learning
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Course Description
Supervised Learning algorithms ,
in this course provides a comprehensive introduction to supervised learning algorithms, a core area of machine learning. You’ll explore key concepts, including how models are trained on labeled data to predict outcomes for new inputs. The course covers popular algorithms such as Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Gradient Boosting methods like XGBoost. Learn how to solve classification and regression problems, evaluate model performance using metrics, and optimize hyperparameters. With practical examples and case studies, this course equips you with the skills to apply supervised learning techniques to real-world tasks such as fraud detection, healthcare analytics, and predictive modeling.
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