Getting Started with Machine Learning

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get started with machine learning; consider reaching out by email if you think an open resource is missing or outdated.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

Reichstein et al. (2019): Deep Learning and Process Understanding for Data-Driven Earth System Science

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

Beucler et al. (2024): Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications

Eyring et al. (2024): AI-empowered next-generation multiscale climate modelling for mitigation and adaptation

Rolnick et al. (2019): Tackling Climate Change with Machine Learning

Willard et al. (2020): Integrating Physics-Based Modeling with Machine Learning: A Survey

Sonnewald et al. (2021): Bridging observations, theory and numerical simulation of the ocean using machine learning

Huntingford et al. (2019): Machine learning and artificial intelligence to aid climate change research and preparedness

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

Irrgang et al. (2021): Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

de Burgh-Day and Leeuwenburg (2023): Machine Learning for numerical weather and climate modelling: a review

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