Machine Learning in Python for Environmental Science Problems AMS 2020 Short Course
- Amanda Burke, University of Oklahoma (aburke1@ou.edu)
- Benjamin Toms, Colorado State University (benatoms@rams.colostate.edu)
- Katherine Avery, University of Oklahoma (katherine.avery@ou.edu)
- Hamid Kamangir, Texas A&M Corpus Christi (hkamangir@islander.tamucc.edu)
- Karthik Kashinath, Lawrence Berkeley National Laboratory (kkashinath@lbl.gov)
- Ryan Lagerquist, University of Oklahoma (ryan.lagerquist@ou.edu)
- Introduction to Machine Learning and AI
- Data Science Fundamentals
- Supervised Learning Algorithms
- Introduction to Deep Learning
- Unsupervised Learning Overview
- Machine Learning Model Interpretation
The modules for this short course require Python 3.6 and the following Python libraries:
- numpy
- scipy
- matplotlib
- xarray
- netcdf4
- pandas
- scikit-learn
- tensorflow-gpu or tensorflow
- keras
- jupyter
- ipython
- jupyterlab
- ipywidgets
The data for the course are stored online. The download_data.py script will download the data to the appropriate location and extract all files. The netCDF data is contained in a 2GB tar file, so make sure you have at least 4GB of storage available and a fast internet connection.
To run the notebooks on the cloud rather than a local installation, see the short course website Machine Learning in Python for Environmental Science.