Getting Started with Machine Learning

Below is a non-exhaustive list of resources to help environmental scientists get started with machine learning; consider reaching out by email if you think a resource is missing or outdated.

Tutorials with Code

Machine Learning Courses

Machine Learning for Environmental Science Courses

Useful Libraries for Machine Learning

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]

Climate Datasets for Machine Learning Research