Ensemble Methods Courses and Certifications

Explore ensemble techniques like bagging, boosting, and stacking to enhance machine learning model performance using random forests and advanced methods.

• Codecademy
• 2 hours
• On-Demand
• Free Trial Available

Yannic Kilcher

Deep Ensembles: A Loss Landscape Perspective

Explore deep ensembles' effectiveness in neural networks, their superiority over Bayesian models, and how they capture non-convex loss landscapes to improve generalization and robustness in AI.

• YouTube
• 47 minutes
• On-Demand
• Free Online Course

echohive

Prototype of a GPT Based Swarm Intelligence System as Example to Inspire More Advanced Swarm Systems

Explore a prototype GPT-based swarm intelligence system, demonstrating ensemble approaches and inspiring advanced swarm development. Includes code reviews, parallelism, and comparisons to true swarm behavior.

• YouTube
• 22 minutes
• On-Demand
• Free Online Course

Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018

Comprehensive machine learning series covering fundamental concepts to advanced topics, taught by Andrew Ng. Includes algorithms, neural networks, and reinforcement learning.

• YouTube
• 1 day 3 hours
• On-Demand
• Free Online Course

Trees, SVM and Unsupervised Learning

Explore support vector machines, neural networks, decision trees, and XG boost for predictive modeling. Learn data representation through PCA and clustering for practical applications in data science.

• Coursera
• 12 hours 58 minutes
• On-Demand
• Free Online Course (Audit)

Regression Analysis

Master regression analysis techniques to model relationships, prevent overfitting, and make data-driven decisions. Explore linear and polynomial regression, regularization, cross-validation, and ensemble methods through hands-on practice.

• Coursera
• 1 day 16 hours
• On-Demand
• Free Online Course (Audit)

Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

64 ratings at Coursera

Explore machine learning's technical aspects, from foundational principles to advanced methods. Learn to avoid pitfalls, interpret models, and evaluate performance while gaining insights into cutting-edge techniques and ethical considerations.

• Coursera
• 17 hours 26 minutes
• On-Demand
• Free Online Course (Audit)

Basics of Machine Learning

"Basics of Machine Learning" introduces participants to the fundamental concepts and tools of machine learning, including probability density estimation, linear regression, classification techniques, ensemble methods, and deep neural networks.

• edX
• 8 weeks, 6-8 hours a week
• On-Demand
• Free Online Course (Audit)

Ensemble Machine Learning in Python: Random Forest, AdaBoost

2372 ratings at Udemy

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

• Udemy
• 5 hours 47 minutes
• On-Demand
• Paid Course

Machine Learning with Imbalanced Data

570 ratings at Udemy

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

• Udemy
• 11 hours
• On-Demand
• Paid Course

Supervised Machine Learning: Classification

342 ratings at Coursera

Learn to train and evaluate classification models, including logistic regression, decision trees, and ensembles. Gain hands-on experience with best practices like handling unbalanced classes and using various error metrics.

• Coursera
• 1 day 58 minutes
• On-Demand
• Free Online Course (Audit)

Sungkyunkwan University

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
• Free Online Course (Audit)

Fractal Analytics

Advanced Machine Learning Algorithms

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
• Free Online Course (Audit)

NPTEL;  IISc Bangalore

Pattern Recognition

Comprehensive exploration of statistical pattern recognition, covering Bayes classifiers, density estimation, linear discriminants, neural networks, SVMs, and advanced learning techniques.

• YouTube
• 1 day 16 hours
• On-Demand
• Free Online Course

NPTEL;  IISc Bangalore

Pattern Recognition

This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier

• YouTube
• 1 day 5 hours
• On-Demand
• Free Online Course