One Day workshop on Advanced Ensemble Learning Techniques for Supervised Machine Learning using Python 2026

This Workshop aims at providing a forum for members of the research and academy community. The Workshop enables the participants to understand the recent technologies in ML Foundations, ensemble techniques such as Boosting, Voting, and Stacking, along with their practical implementation using Python. The One-Day Workshop on Advanced Ensemble Learning Techniques for Supervised Machine Learning aims to provide participants with a comprehensive understanding of the fundamental concepts, advanced methodologies, and practical applications of ensemble learning.

The workshop will introduce the principles of ensemble modeling and explain how combining multiple base learners can enhance the accuracy and reliability of predictive models. Participants will explore major ensemble techniques, including Bagging, Random Forest, Boosting, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Voting Classifiers, and Stacking.The workshop will emphasize the theoretical foundations and practical implementation of these techniques, highlighting their strengths, limitations, and suitability for different supervised learning tasks. Participants will gain insights into model selection, hyperparameter tuning, feature importance analysis, and performance evaluation using appropriate metrics for classification and regression problems. A hands-on session using Python and machine learning libraries such as Scikit-learn will enable participants to implement ensemble algorithms, experiment with real-world datasets, and compare the performance of individual models with ensemble-based approaches.

• Events & highlights
• ·ML Foundations

·Ensemble techniques

·Boosting

·Voting

·Stacking

·Practical implementation using Python

·AdaBoost

· Gradient Boosting

·XGBoost and ·Random Forest