Bagging Courses and Certifications

Decision Trees, Random Forests, Bagging & XGBoost: R Studio

242 ratings at Udemy

Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming

• Udemy
• 5 hours 56 minutes
• On-Demand
• Paid Course

Ensemble Methods in Python

1 review

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Great Learning

Bagging and Boosting

Explore ensemble learning techniques like bagging and boosting to improve machine learning model performance. Learn how to combine multiple algorithms for better results across various domains.

• YouTube
• 1 hour 35 minutes
• On-Demand
• Free Video

Software Engineering Courses - SE Courses

Logistic Regression and Ensemble Learning - Bagging and Boosting - AdaBoost

Explore logistic regression, ensemble learning, bagging, and boosting in this AI/ML course. Learn AdaBoost and decision stumps for strong non-linear classification.

• YouTube
• 44 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Bagging (Bootstrap Aggregating)

Explore the concept of Bagging (Bootstrap AGGregatING), its working, hyperparameters, OOB error estimation, advantages, limitations, and relationship to Random Forests.

• YouTube
• 12 minutes
• On-Demand
• Free Video

Ensemble Methods from Scratch

Master ensemble methods by building Bagging, Random Forest, AdaBoost, and Gradient Boosting implementations from scratch. Learn to enhance predictive models through hands-on coding without relying on libraries.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Ensembles in Machine Learning

Master ensemble learning techniques like bagging, boosting, and stacking to enhance predictive modeling performance, from Random Forests to Gradient Boosting and AdaBoost implementations.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Ensemble Methods

Master ensemble methods by implementing Bagging, Random Forest, AdaBoost, and Stacking from scratch in C++ using mlpack for robust machine learning model aggregation.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Ensemble Machine Learning in Python: Random Forest, AdaBoost

2653 ratings at Udemy

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

• Udemy
• 5 hours 48 minutes
• On-Demand
• Paid Course

Fractal Analytics

Advanced Machine Learning Algorithms

12 ratings at Coursera

Explore advanced machine learning algorithms, from regularization to ensemble methods. Master feature engineering, hyperparameter tuning, and model selection for enhanced predictive accuracy and real-world applications.

• Coursera
• 20 hours 43 minutes
• On-Demand
• Paid Course

Machine Learning for Smart Beta

Explore smart beta products, recreate MSCI Enhanced Value Index, and apply machine learning techniques to create improved multi-factor models for investment strategies using R programming.

• Coursera
• 7 hours 30 minutes
• On-Demand
• Paid Course

Coursera

Advanced Machine Learning Techniques

Master ensemble methods, dimensionality reduction, NLP, reinforcement learning, and AutoML using industry-standard tools like Scikit-learn, XGBoost, and PyTorch for real-world AI applications.

• Coursera
• 1 day 9 hours 42 minutes
• On-Demand
• Paid Course

Coursera

Improve Accuracy with ML Ensemble Methods

Master ensemble techniques like bagging, boosting, and random forests to enhance ML model accuracy and reliability using Java in hands-on labs and real-world projects.

• Coursera
• 3 hours 55 minutes
• On-Demand
• Paid Course

Coursera

Optimize AI: Build Robust Ensemble Models

Unlock advanced ensemble modeling techniques to balance performance with interpretability while mastering statistical validation for production AI systems.

• Coursera
• 1 hour 55 minutes
• On-Demand
• Paid Course

Packt

Deep Learning and Advanced Techniques

Master advanced deep learning with ensemble methods, neural networks, and frameworks like TensorFlow, PyTorch, XGBoost for real-world AI applications and model deployment.

• Coursera
• 9 hours 23 minutes
• On-Demand
• Paid Course