Para celebrar el día del libro os voy a dejar un recopilatorio de libros libres (y toturiales extensos) que están disponibles online y que se pueden ejecutar en la nube para que los puedas seguir sin necesidad de instalar nada:

• Mining the social web (3ªedición, Matthew Russell y Mikhail Klassen) (github) (binder).
• Python Data Science cookbook (Jake VanderPlas) (github) (binder) (colab).
• Hands-on machine learning with scikit-learn, Tensorflow and Keras (Aurelien 2ª edición, Geron) (github) (binder) (colab).
• Kalman and bayesian filters in python (Roger R Labbe Jr) (github) (binder) (azure) (pdf).
• 12 steps to Navier-Stokes (Lorena Barba) (github) (binder).
• Python for probability, statistics and machine learning (José Unpingco) (github) (binder).
• Digital Signal Processing - Theory and Computational Examples (Sascha Spors) (github) (binder).
• Hands-on NLTK tutorial (HZ Sababa) (github) (binder).
• The climate laboratory: A hands-on approach to climate physics and climate modelling (Brian E.J. Rose) (Github) (JupyterBook).
• Riemann problems and Jupyter solutions (David I. Ketcheson, Randall J. LeVeque y Mauricio del Razo Sarmina) (github) (binder).
• Advanced NLP with SpaCy (Ines Montani) (github) (binder) (executable web page).
• Lectu()res on scientific computing with Python (JR Johansson) (github) (azure).
• Numerical computing is fun (Mikko Kotila) (github) (binder) (azure).
• Introduction to Python for Computational Science and Engineering (Hans Fangohr) (Github) (Binder) (pdf).
• Notes on Scientific Computing for Biomechanics and Motor Control (Marcos Duarte and Renato Watanabe) (github) (binder).
• PyTherm - Applied thermodynamics (Iury Segtovich) (github) (binder).
• An Introduction to Applied Bioinformatics (Greg Caporaso) (github) (binder) (web).
• Materials Science Jupyter Notebooks (github) (binder) (web).
• Python for geosciences (Nikolay Koldunov) (github) (binder).

Espero que los disfrutes.

Si conoces otros puedes dejar un comentario con la información.