This repository is a comprehensive awesome list focused on imbalanced learning, a subfield of machine learning dealing with skewed class distributions. It aggregates papers, code implementations, frameworks, and libraries covering techniques like resampling, cost-sensitive learning, ensemble methods, and long-tail distribution handling. The collection spans both deep learning and classical ML approaches, with resources categorized across different methods and application domains.