Explore the second stage of the machine learning process in this 36-minute video lecture focusing on collecting and curating training data to inform physics-based models. Learn about data augmentation techniques incorporating known symmetries, the importance of coordinate systems, and the differences between simulated and experimental data. Discover the balance between big data and diverse data, strategies for generalizing models with physics, and the challenges of expensive and biased data collection. Delve into handling rare events, small signals, and hidden variables in physics-informed machine learning. Gain insights into discovering governing equations and the concept of digital twins. Enhance your understanding of AI/ML applications in physics through this comprehensive exploration of data curation techniques.