Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.

End-to-end MLOps pipeline showcasing senior-level best practices with Hydra for configuration, MLflow for experiment tracking, Optuna for hyperparameter tuning, and DVC for data/version control. This repository focuses on reproducibility, modular design, and streamlined collaboration—an ideal demonstration of advanced MLOps capabilities.