This repository contains Jupyter notebooks with code examples demonstrating machine learning design patterns from the O’Reilly book. The content covers data representation patterns like embeddings and feature crosses, problem representation patterns including ensemble and cascade methods, model training patterns such as transfer learning and hyperparameter tuning, and responsible AI concepts. It serves as a practical educational resource for ML practitioners seeking to apply established design patterns in their work.