The practical side of a university machine learning course: hands-on Jupyter notebooks I wrote and taught as an Assistant Professor, where students moved from theory to working code in each session.
The materials run as sequential practical classes, each a self-contained notebook covering a stage of the machine learning curriculum, built so students try the algorithms themselves rather than only see them on slides.
Teaching materials in Spanish, built on Python’s scientific stack.