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get started with machine learning; consider reaching out by email if you think an open resource is missing or outdated.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

Machine Learning for Environmental Science Courses

Machine Learning in Weather & Climate: Massive Open Online Course designed by ECMWF,, 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
Textbooks and Pedagogical Articles

Géron (2019): Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow [code1/code2/code/pdf]

Chollet (2017): Deep Learning with Python [code/pdf]

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