The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms. The book was developed by and initially for our multidisciplinary M²LInES members, composed of machine learning experts, climate scientists, and numerical model developers. We hope you find it useful for all your research and educational needs!