[Download] Machine Learning with Imbalanced Data For Free
What you’ll learn
- 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
Requirements
- Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
- Python programming, including familiarity with NumPy, Pandas and Scikit-learn
- A Python and Jupyter notebook installation
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