Machine Learning with Imbalanced Data

• Apply random under-sampling to remove observations from majority classes
• Perform under-sampling by removing observations that are hard to classify
• Carry out under-sampling by retaining observations at the boundary of class separation
• Apply random over-sampling to augment the minority class
• Create syntethic data to increase the examples of the minority class
• Implement SMOTE and its variants to synthetically generate data
• Use ensemble methods with sampling techniques to improve model performance
• Change the miss-classification cost optimized by the models to accomodate minority classes
• Determine model performance with the most suitable metrics for imbalanced datasets

Who this course is for:

• Data scientists and machine learning engineers working with imbalanced datasets
• Data scientists who want to improve the performance of models trained on imbalanced datasets
• Students who want to learn intermediate content on machine learning
• Students working with imbalanced multi-class targets