[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