Ensemble Learning Courses and Certifications

UofU Data Science

Boosting and Ensemble Methods in Machine Learning - Lecture 18

Dive into boosting algorithms and learn how to combine weak learning rules to create powerful predictive models in machine learning applications.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning: Boosting and Ensemble Methods - Lecture 19

Dive into advanced boosting techniques and ensemble methods to transform weak learning algorithms into powerful predictive models for enhanced machine learning capabilities.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Career Certificate

Implementing AI Algorithms from Scratch

67 ratings at CodeSignal

Implement classic machine learning algorithms from scratch—including regression, classification, clustering, ensemble methods, optimization, and neural networks—without high-level libraries.

• CodeSignal
• Self-Paced
• Free Certificate
• 6 courses

Intro to Model Optimization in Machine Learning

Tune and evaluate classifiers on the Wisconsin Breast Cancer Dataset: GridSearchCV hyperparameter search, L1/L2 regularization, RandomForest and Gradient Boosting, feature scaling, and performance metrics.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Introduction to Machine Learning with Gradient Boosting Models

Build and refine a Gradient Boosting Regressor to forecast Tesla stock price changes using cross-validation, GridSearchCV, feature importance, and early stopping.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Advanced Modeling for Text Classification

Build advanced text classifiers: bagging, voting, and gradient boosting ensembles in Python, then tokenize text and train neural networks and simple RNNs with TensorFlow.

• CodeSignal
• 6 hours
• Self-Paced
• Free Certificate

Foundations of Gradient Boosting

Build and tune gradient boosting models by progressing from decision trees through ensembles, monitoring learning, comparing approaches, and analyzing feature importance.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

XGBoost for Beginners

Build and optimize XGBoost models using complexity controls, early stopping, evaluation metrics, and automated grid search.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

LightGBM Made Simple

Explore LightGBM’s leaf-wise growth, histogram algorithms, categorical handling, and parameter tuning through hands-on experiments.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Career Certificate

Mastering Gradient Boosting with XGBoost & LightGBM

Learn to build, tune, and interpret gradient-boosting classifiers with scikit-learn, XGBoost, LightGBM, and CatBoost.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate
• 3 courses

BSidesLV

Advancing Network Threat Detection Through Standardized Feature Extraction and Dynamic Ensemble Learning

Discover how standardized feature extraction and dynamic ensemble learning achieve 97.92% accuracy in network threat detection while minimizing false positives for resilient defenses.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Evaluation Metrics & Advanced Techniques for Imbalanced Data

Evaluate and improve machine learning models on imbalanced datasets using specialized metrics, class weights, ensemble methods, and anomaly detection.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Career Certificate

Implementing AI Algorithms from Scratch in C++ (Part 2)

Implement ensemble, unsupervised, clustering, and neural-network algorithms from scratch in C++ for hands-on AI development.

• CodeSignal
• 8 hours
• Self-Paced
• Free Certificate
• 3 courses

Career Certificate

Feature Engineering with Kaggle Dataset

Build predictive models by transforming Kaggle data into effective features and combining optimized learners through weighted ensembles.

• CodeSignal
• 6 hours
• Self-Paced
• Free Certificate
• 3 courses

DeepLearning.AI;  Stanford

Advanced Learning Algorithms

8791 ratings at Coursera

Build and train neural networks in TensorFlow for multiclass classification, tune models with ML best practices, and apply decision trees, random forests, and boosted trees.

• Coursera
• 4 weeks, 9 hours a week
• Self-Paced
• Paid Course