Class Imbalances Courses and Certifications

Handling Unbalanced Datasets

Learn to detect class imbalance and improve model performance through undersampling, oversampling, SMOTE, ADASYN, and resampling pipelines.

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

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

Hands-on Approaches to Handling Data Imbalance

Learn to handle imbalanced machine-learning datasets through data preparation, baseline modeling, resampling, evaluation metrics, and specialized algorithms for robust, fair models.

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

Machine Learning with Imbalanced Data

Improve classifiers trained on imbalanced data: apply under-sampling, SMOTE and other over-sampling methods, cost-sensitive learning, ensembles, and metrics like ROC-AUC and balanced accuracy.

• Train in Data
• Self-Paced
• Paid Course

Julia Silge

Class Imbalance and Classification Metrics With Aircraft Wildlife Strikes

Using aircraft wildlife-strike data, this screencast shows how balancing an imbalanced classification problem with SMOTE changes accuracy, log loss, sensitivity, and specificity.

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

AI Workflow: Feature Engineering and Bias Detection

86 ratings at Coursera

Apply feature engineering best practices: handle class imbalance, detect model bias with AI Fairness 360, reduce dimensions, flag outliers, and explore text through topic modeling.

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

Julia Silge

Impute Missing Data and Handle Class Imbalance for Himalayan Climbing Expeditions

Use tidymodels in R to predict survival on Himalayan climbing expeditions while imputing missing data and addressing class imbalance.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Open Data Science

Robustness to Adversarial Inputs and Tail Risk via Boosting - Machine Learning Model Defense Strategies

Explore innovative strategies for robust machine learning models against adversarial inputs and tail risks, focusing on ensemble methods and risk-sensitive decision-making in high-stakes environments.

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

Data Con LA

Predicting Rare Events Using Ordinal Regression - From Purchases to Diseases

Dive into advanced ordinal regression techniques for predicting rare events like purchases and diseases, with practical examples and implementation guidance.

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

Cloud Security Alliance

Machine Learning for Threat Classification: Addressing Class Imbalance and Explainability

Explore techniques for class imbalance, explainability, and best practices in machine learning for threat classification modeling.

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

NDC Conferences

Garbage Data In, Garbage Models Out - How to Select the Right Data for Robust Machine Learning

Explore common pitfalls in creating datasets for machine learning, learn to select appropriate data, and design experiments for robust models that generalize well to new information.

• YouTube
• 46 minutes
• Self-Paced
• Conference Talk

Handling Imbalanced Data Classification Problems

56 ratings at Coursera

Build classifiers on imbalanced datasets: apply undersampling, oversampling and SMOTE, pick suitable evaluation metrics, and use ROC curves to tune the probability threshold.

• Coursera
• 1 hour 55 minutes
• No Longer Offered
• Paid Course