Ensemble Learning Courses and Certifications

Découvrez le bagging, les forêts aléatoires et le gradient boosting pour construire des modèles d’ensemble performants.

• OpenClassrooms
• 15 hours
• Self-Paced
• Free Certificate

Introduction to Machine Learning in Sports Analytics

28 ratings at Coursera

Apply supervised learning methods — SVMs, decision trees, random forests, ensembles — with Python's scikit-learn to predict athletic outcomes from NHL, MLB, and wearable device data.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Advanced Methods in Machine Learning Applications

Apply ensemble methods, regression, clustering, and reinforcement learning by building random forests, coding logistic regression from scratch, running k-means and DBSCAN, and implementing Q-learning.

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

The Nuts and Bolts of Machine Learning

646 ratings at Coursera

Build supervised and unsupervised models in Python: Naive Bayes, K-means clustering, decision trees, random forest bagging and XGBoost boosting, plus feature engineering and parameter tuning.

• Coursera
• 5 weeks, 7 hours a week
• Self-Paced
• Paid Course

Supervised Machine Learning: Classification

469 ratings at Coursera

Train classification models — logistic regression, k-nearest neighbors, support vector machines, decision trees and tree ensembles — and handle unbalanced classes with oversampling and undersampling.

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

Introduction to Machine Learning: Supervised Learning

55 ratings at Coursera

Learn to build and evaluate supervised models in Python: linear and logistic regression, k-nearest neighbors, cross-validation, Ridge and Lasso, decision trees, and ensembles.

• Coursera
• 5 weeks, 5 hours a week
• Self-Paced
• Paid Course

Trees, SVM and Unsupervised Learning

10 ratings at Coursera

Build predictive models with support vector machines, neural networks, decision trees, bagging and random forests, and generate data representations through PCA and clustering.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Regression Analysis

10 ratings at Coursera

Learn regression as supervised learning: fit linear and polynomial models, apply Ridge, Lasso and Elastic Net, run cross-validation, and combine models with bagging, boosting and stacking.

• Coursera
• 6 weeks, 7 hours a week
• Self-Paced
• Paid Course

Coursera

Mining Quality Prediction Using Machine & Deep Learning

70 ratings at Coursera

Predict silica content in iron ore concentrate: train linear regression, decision tree, random forest, gradient boosting and neural network models in Python and compare MSE, RMSE, R2.

• Coursera
• 1 hour 55 minutes
• Self-Paced
• Paid Course

Machine Learning and AI with Python

26 ratings at edX

Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.

• edX
• 6 weeks, 4-5 hours a week
• Self-Paced
• Free Online Course (Audit)

Fractal Analytics

Advanced Machine Learning Algorithms

14 ratings at Coursera

Apply regularization, bagging with random forests and extra trees, boosting with AdaBoost, XGBoost, LightGBM and CatBoost, plus feature engineering, hyperparameter tuning, stacking and model selection.

• Coursera
• 6 weeks, 3 hours a week
• Self-Paced
• Paid Course

Machine Learning for Smart Beta

Rebuild the MSCI Enhanced Value Index in R, then improve smart beta multifactor portfolios with CART, bagging, boosting and ensemble methods.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Advanced Machine Learning Techniques

Implement bagging, boosting and stacking, reduce dimensions with PCA, t-SNE and UMAP, build NLP and reinforcement learning models, and automate tuning with AutoML and MLflow.

• Coursera
• 5 weeks, 7 hours a week
• Self-Paced
• Paid Course

Coursera

Optimizing and Governing AI Systems

Optimize and govern production AI: run statistical A/B tests, build bagging and stacking ensembles, explain models with SHAP and LIME, and design enforceable governance frameworks.

• Coursera
• 13 weeks, 1 hour a week
• Self-Paced
• Paid Course

Packt

Machine Learning: Random Forest with Python from Scratch©

Build a Random Forest model from scratch in Python: learn data types and loops, then handle missing values, encode categorical data, and tune for accuracy.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course