Ensemble Methods Courses and Certifications

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

Credential

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

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

Credential

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

Supervised Machine Learning: Classification

459 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 17 minutes
• Self-Paced
• Paid Course

Data Analytics Foundations for Accountancy II

11 ratings at Coursera

Explore machine learning concepts, algorithms, and practical applications in data analytics using Python. Gain skills in classification, regression, clustering, and anomaly detection for business insights.

• Coursera
• 2 days 22 hours 1 minute
• Self-Paced
• Paid Course

Regression Analysis

10 ratings at Coursera

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 1 minute
• Self-Paced
• Paid 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 59 minutes
• Self-Paced
• Paid Course

Advanced AI and Machine Learning Techniques and Capstone

45 ratings at Coursera

Master advanced AI techniques, from ensemble methods to ethical GenAI implementation, while building scalable systems and completing a real-world capstone project using Microsoft Azure.

• Coursera
• 1 day 8 hours 57 minutes
• Self-Paced
• Paid Course

Coursera Credential

Machine Learning with Scikit-learn, PyTorch & Hugging Face

28 ratings at Coursera

Master machine learning from foundations to generative AI using Python, scikit-learn, PyTorch & Hugging Face through hands-on projects and real-world applications.

• Coursera
• 20 weeks, 7 hours/week
• Self-Paced
• Paid Course
• 5 courses

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

1039 ratings at Udemy

Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python

• Udemy
• 7 hours 20 minutes
• Self-Paced
• Paid Course

EDUCBA

Machine Learning with R: Build, Analyze & Predict

16 ratings at Coursera

Master machine learning foundations and R programming through hands-on practice with regression, classification, decision trees, and ensemble methods for real-world data analysis.

• Coursera
• 12 hours 31 minutes
• Self-Paced
• 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
• Self-Paced
• Paid Course

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/week
• Self-Paced
• Free Online Course (Audit)