Below is a non-exhaustive list of open resources to help environmental scientists get started with machine learning; consider reaching out by email if you think an open resource is missing or outdated.
Tutorials with Code
Machine Learning Courses
Coursera Machine Learning: Beginner courses in machine learning
fast.ai: Online deep learning course
DeepLearning.AI: Online deep learning course
Stanford CS20: Tensorflow/Keras course
Stanford CS231n: Popular course in deep learning for computer vision
paperswithcode.com: Trending machine learning articles with code
Machine Learning for Environmental Science Courses
Machine Learning in Weather & Climate: Massive Open Online Course designed by ECMWF, competence.lu, and various experts (including ∂AWN)
Applied Machine Learning Tutorial for Earth Scientists: Short hands-on course designed by the Barnes Research Group
Machine Learning for Weather and Climate: Short course designed by CIRA for atmospheric science researchers
Explainable Artificial Intelligence for Environmental Science: Short course designed for AI2ES/CIRA researchers
Artificial Intelligence for Earth Monitoring: Hands-on course from the Copernicus Programme for students
To Get Started before Machine Learning
An Introduction to Earth and Environmental Data Science: Basics of research computing in Earth science for students
Pythia Foundations: A community learning resource for Python-based computing in the geosciences
Programming with Python by © Software Carpentry and © Data Carpentry: To focus on the fundamentals at a relaxed pace
Useful Libraries for Machine Learning
Scikit-learn Tutorial: Python library for scientific machine learning
Tensorflow Tutorial: Python library for deep learning
PyTorch Tutorial: Python library for deep learning
MLib Tutorial: Spark library for machine learning
Literature Reviews on Machine Learning for Environmental Science
Eyring et al. (2024): Pushing the frontiers in climate modeling and analysis with machine learning
Beucler et al. (2021): Machine Learning for Clouds and Climate
Lai et al. (2025): Machine Learning for Climate Physics and Simulations
Bergen et al. (2019): Machine Learning for Data-Driven Discovery in Solid Earth Geoscience
Rampal et al. (2024): Enhancing Regional Climate Downscaling through Advances in Machine Learning
Bracco et al. (2024): Machine learning for the physics of climate
Rolnick et al. (2019): Tackling Climate Change with Machine Learning
Willard et al. (2020): Integrating Physics-Based Modeling with Machine Learning: A Survey
Molina et al. (2023): A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena
Chantry et al. (2021): Machine Learning for Weather and Climate Modelling
Raghu and Schmidt (2020): A Survey of Deep Learning for Scientific Discovery
Textbooks and Pedagogical Articles
Géron (2019): Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow [code]
Chollet (2017): Deep Learning with Python [code]
Molnar (2021): Interpretable Machine Learning [website]
Goodfellow et al. (2016): Deep Learning [website]
Foster (2019): Generative Deep Learning [website/code]
Nielsen (2019): Neural Networks and Deep Learning [website/code]
Parr and Howard (2018): The Matrix Calculus you Need for Deep Learning [pdf]
James et al. (2013): An Introduction to Statistical Learning [website/pdf/code]
Hastie et al. (2017): The Elements of Statistical Learning [website/pdf]
Bishop (2006): Pattern Recognition and Machine Learning [pdf/code/Matlab]
Environmental Datasets for Machine Learning Research
List of benchmark datasets (maintained by Pangeo)
Open datasets for machine learning (maintained by Kaggle)