Ensemble Methods Courses and Certifications

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

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
• Self-Paced
• Free Certificate

Hypertuning and Cross-Validation

Master hyperparameter optimization techniques through grid search, random search, and cross-validation methods to enhance machine learning model performance and achieve optimal results.

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

Advanced Modeling for Text Classification

Master advanced text classification using ensemble methods, deep learning, and neural networks with Python and TensorFlow. Build robust models using bagging, voting classifiers, gradient boosting, and RNNs for improved performance.

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

Career Certificate

Mastering Gradient Boosting with XGBoost & LightGBM

Master gradient boosting techniques using XGBoost, LightGBM, and CatBoost to build, tune, and interpret powerful models for real-world classification problems with hands-on practice.

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

Foundations of Gradient Boosting

Master gradient boosting by building decision trees, creating ensembles, and understanding feature importance through hands-on banking data analysis and model optimization.

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

XGBoost for Beginners

Master XGBoost fundamentals to build faster, accurate models with parameter tuning, early stopping techniques, and native interface configuration for optimal performance.

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

Evaluation Metrics & Advanced Techniques for Imbalanced Data

Master evaluation metrics and advanced techniques for handling imbalanced datasets, including cost-sensitive learning, ensemble methods, and anomaly detection strategies.

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

Career Certificate

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

Master ensemble methods, unsupervised learning, clustering, and neural networks by implementing AI algorithms from scratch in C++ using mlpack for hands-on expertise.

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

Career Certificate

Python Machine Learning Advanced

Master advanced machine learning techniques using Python, including deep learning, ensemble methods, and model optimization for complex real-world applications.

• VDCI
• 30 hours
• Self-Paced
• $1,895.00

Career Certificate

Feature Engineering with Kaggle Dataset

Master feature engineering using Kaggle datasets, progressing from baselines to advanced optimizations for Linear Regression, Random Forest, and LightGBM, while building weighted ensembles for peak predictive performance.

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

Machine Learning Foundations for Product Managers

831 ratings at Coursera

Build foundational understanding of machine learning, including model development, evaluation, and common algorithms. Gain hands-on experience training and optimizing a model for a real-world problem.

• Coursera
• 16 hours 2 minutes
• Self-Paced
• Paid Course

Introduction to Machine Learning in Sports Analytics

27 ratings at Coursera

Explore supervised machine learning techniques using Python and real athletic data to predict sports outcomes, applying methods like SVM, decision trees, and ensembles to analyze professional leagues and wearable device data.

• Coursera
• 12 hours 55 minutes
• Self-Paced
• Paid Course

Career Certificate

Applied Machine Learning

16 ratings at Coursera

Develop practical machine learning skills through hands-on application of supervised learning, ensemble methods, neural networks, and advanced techniques like CNNs and reinforcement learning.

• Coursera
• 12 weeks, 5 hours/week
• Self-Paced
• Paid Course
• 3 courses

Advanced Methods in Machine Learning Applications

Master advanced machine learning techniques for complex problem-solving. Explore ensemble learning, regression analysis, unsupervised learning, and reinforcement learning. Gain practical skills to implement and optimize sophisticated models.

• Coursera
• 19 hours 58 minutes
• Self-Paced
• Paid Course