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ML_for_Environmental_Science

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A non-exhaustive list of open resources to help environmental scientists get started with machine learning

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# 🌍 Open Resources for Environmental Scientists to Get Started with Machine Learning

Welcome to this curated collection of open resources designed to assist environmental scientists in embarking on their machine learning journey! If you notice missing or outdated open resources, feel free to submit a pull request.

## 📚 Table of Contents
- [Tutorials with Code](#tutorials-with-code)
  - [Machine Learning Courses](#machine-learning-courses)
  - [Machine Learning for Environmental Science Courses](#machine-learning-for-environmental-science-courses)
  - [To Get Started Before Machine Learning](#to-get-started-before-machine-learning)
  - [Useful Libraries for Machine Learning](#useful-libraries-for-machine-learning)
- [Literature Reviews on Machine Learning for Environmental Science](#literature-reviews-on-machine-learning-for-environmental-science)
- [Textbooks and Pedagogical Articles](#textbooks-and-pedagogical-articles)
- [Environmental Datasets for Machine Learning Research](#environmental-datasets-for-machine-learning-research)

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## Tutorials with Code

### Machine Learning Courses
- [Coursera Machine Learning](https://www.coursera.org/learn/machine-learning): Beginner courses in machine learning.
- [fast.ai](http://fast.ai): Online deep learning course.
- [DeepLearning.AI](http://deeplearning.ai): Online deep learning course.
- [Stanford CS20](https://web.stanford.edu/class/cs20si/syllabus.html): TensorFlow/Keras course.
- [Stanford CS231n](http://cs231n.stanford.edu/): Popular course in deep learning for computer vision.
- [Papers with Code](https://paperswithcode.com/): Trending machine learning articles with code.

### Machine Learning for Environmental Science Courses
- [Machine Learning in Weather & Climate](https://learning.ecmwf.int/course/index.php?categoryid=1): MOOC designed by ECMWF, competence.lu, and ∂AWN.
- [Machine Learning for Earth & Environmental Sciences](https://tbeucler.github.io/2024_MLEES_Ebook/): Hands-on course by the ∂AWN group.
- [Applied Machine Learning Tutorial for Earth Scientists](https://github.com/eabarnes1010/ml_tutorial_csu): Hands-on course by the Barnes Research Group.
- [Machine Learning for Weather and Climate](https://docs.google.com/document/d/1SPNxZrbHMaIEaS2dbntDow9x_tgSuFTUTOugfa2NuRo/edit): CIRA's short course for atmospheric science researchers.
- [Explainable Artificial Intelligence for Environmental Science](https://docs.google.com/document/d/1lqpABwDl3kPe6ThE-NIDR64PimnltJEuKNkysDZuWKQ/edit): AI2ES/CIRA course.
- [Artificial Intelligence for Earth Monitoring](https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring): Hands-on course from the Copernicus Programme.

### To Get Started Before Machine Learning
- [An Introduction to Earth and Environmental Data Science](https://earth-env-data-science.github.io/intro.html): Basics of research computing in Earth sciences.
- [Pythia Foundations](https://foundations.projectpythia.org/landing-page.html): Community learning for Python-based geoscience computing.
- [Programming with Python](https://swcarpentry.github.io/python-novice-inflammation/): Software and Data Carpentry tutorials for Python fundamentals.

### Useful Libraries for Machine Learning
- [Scikit-learn User Guide](https://scikit-learn.org/stable/user_guide): Scientific ML in Python.
- [TensorFlow Tutorial](https://www.tensorflow.org/tutorials): Tutorials for deep learning.
- [PyTorch Tutorial](https://pytorch.org/tutorials/): Tutorials for deep learning.
- [MLlib Tutorial](https://spark.apache.org/docs/latest/ml-guide.html): Spark's ML library guide.

---

## Literature Reviews on Machine Learning for Environmental Science
- [Stephan Rasp (Living Review)](https://docs.google.com/spreadsheets/d/1n30zDDjEzlXl5nAGF8uD_dbZWJAamqImQGCZjfOMuDg/edit?gid=0#gid=0): *State-of-the-art in AI-based weather forecasting*.
- [Tom Beucler (Living Review)](https://github.com/tbeucler/HybridESM): *State-of-the-art in hybrid Earth system modeling*.
- [Eyring et al. (2024)](https://www.nature.com/articles/s41558-024-02095-y): *Pushing the frontiers in climate modeling and analysis with machine learning*.
- [Beucler et al. (2021)](https://www.authorea.com/doi/full/10.1002/essoar.10506925.1): *Machine Learning for Clouds and Climate*.
- [Ullrich et al. (2025)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JH000496): *Recommendations for comprehensive and independent evaluation of machine learning-based Earth system models*.
- [Reichstein et al. (2019)](https://www.nature.com/articles/s41586-019-0912-1): *Deep Learning and Process Understanding for Data-Driven Earth System Science*.
- [Lai et al. (2025)](https://www.annualreviews.org/content/journals/10.1146/annurev-conmatphys-043024-114758): *Machine Learning for Climate Physics and Simulations*.  
- [Tebaldi et al. (2025)](https://essopenarchive.org/doi/full/10.22541/essoar.174721805.59111383/v1): *Emulators of climate model output*.
- [Hickman et al. (2025)](https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3739/): *Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research*.
- [Zhu et al. (2023)](https://pubs.acs.org/doi/10.1021/acs.est.3c00026): *Machine Learning in Environmental Research: Common Pitfalls and Best Practices*.
- [Reichstein et al. (2025)](https://www.nature.com/articles/s41467-025-57640-w): *Early warning of complex climate risk with integrated artificial intelligence*.
- [Bergen et al. (2019)](https://science.sciencemag.org/content/363/6433/eaau0323): *Machine Learning for Data-Driven Discovery in Solid Earth Geoscience*.
- [Rampal et al. (2024)](https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml): *Enhancing Regional Climate Downscaling through Advances in Machine Learning*.
- [Beucler et al. (2024)](https://arxiv.org/abs/2311.13691): *Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications*.
- [Camps-Valls et al. (2025)](https://www.nature.com/articles/s41467-025-56573-8): *Artificial intelligence for modeling and understanding extreme weather and climate events*.
- [Bracco et al. (2024)](https://www.nature.com/articles/s42254-024-00776-3): *Machine learning for the physics of climate*.
- [Eyring et al. (2024)](https://www.nature.com/articles/s41561-024-01527-w): *AI-empowered next-generation multiscale climate modelling for mitigation and adaptation*.
- [Rolnick et al. (2019)](https://arxiv.org/abs/1906.05433): *Tackling Climate Change with Machine Learning*.
- [Willard et al. (2020)](https://arxiv.org/abs/2003.04919): *Integrating Physics-Based Modeling with Machine Learning: A Survey*.
- [Sonnewald et al. (2021)](https://iopscience.iop.org/article/10.1088/1748-9326/ac0eb0): *Bridging observations, theory and numerical simulation of the ocean using machine learning*.
- [Huntingford et al. (2019)](https://iopscience.iop.org/article/10.1088/1748-9326/ab4e55): *Machine learning and artificial intelligence to aid climate change research and preparedness*.
- [Molina et al. (2023)](https://journals.ametsoc.org/view/journals/aies/2/4/AIES-D-22-0086.1.xml): *A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena*.
- [Chantry et al. (2021)](https://royalsocietypublishing.org/toc/rsta/2021/379/2194): *Machine Learning for Weather and Climate Modelling*.
- [Irrgang et al. (2021)](https://www.nature.com/articles/s42256-021-00374-3): *Towards neural Earth system modelling by integrating artificial intelligence in Earth system science*.
- [de Burgh-Day and Leeuwenburg (2023)](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-350/): *Machine Learning for numerical weather and climate modelling: a review*.
- [Raghu and Schmidt (2020)](https://arxiv.org/abs/2003.11755): *A Survey of Deep Learning for Scientific Discovery*.

---

## Textbooks and Pedagogical Articles
- Géron (2019): *Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow* ([code](https://github.com/ageron/handson-ml3)).
- Chollet (2017): *Deep Learning with Python* ([code](https://github.com/fchollet/deep-learning-with-python-notebooks)).
- Molnar (2021): *Interpretable Machine Learning* ([website](https://christophm.github.io/interpretable-ml-book/)).
- Goodfellow et al. (2016): *Deep Learning* ([website](http://www.deeplearningbook.org/)).
- Foster (2019): *Generative Deep Learning* ([website](https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/), [code](https://github.com/davidADSP/GDL_code)).
- Nielsen (2019): *Neural Networks and Deep Learning* ([website](http://neuralnetworksanddeeplearning.com/), [code](https://github.com/mnielsen/neural-networks-and-deep-learning)).
- Parr and Howard (2018): *The Matrix Calculus You Need for Deep Learning* ([pdf](https://arxiv.org/pdf/1802.01528)).
- James et al. (2013): *An Introduction to Statistical Learning* ([website](https://www.statlearning.com/), [code](https://www.statlearning.com/resources-first-edition)).
- Hastie et al. (2017): *The Elements of Statistical Learning* ([website](https://web.stanford.edu/~hastie/ElemStatLearn/)).
- Bishop (2006): *Pattern Recognition and Machine Learning* ([pdf](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf), [code](https://github.com/gerdm/prml), [Matlab](https://github.com/PRML/PRMLT)).

---

## Environmental Datasets for Machine Learning Research
- [CCAI Climate-related Datasets](https://wiki.climatechange.ai/wiki/Climate-related_datasets): Weather and climate datasets that can be used for ML applications.
- [Pangeo Weather and Climate Datasets](http://mldata.pangeo.io/index.html): Datasets for environmental ML research.
- NeurIPS Datasets and Benchmarks: [2024](https://nips.cc/virtual/2024/events/datasets-benchmarks-2024) [2023](https://nips.cc/virtual/2023/events/datasets-benchmarks-2023) [2022](https://nips.cc/virtual/2022/events/datasets-benchmarks-2022) [2021](https://nips.cc/virtual/2021/events/Datasets%20and%20Benchmarks)
- [Kaggle Datasets](https://www.kaggle.com/datasets): Open datasets for machine learning.
- [Hugging Face](https://huggingface.co/): Large open datasets for machine learning.

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