Learning Machine Learning for Astronomy
A beginner asks for free resources, tutorials, books, and existing Jupyter notebooks for applying machine learning to astronomy. Potential use cases include analyzing JWST or TESS data to identify black-hole or exoplanet signatures and building reproducible environments with Git, Python libraries, and Docker.








