MOOC Machine Learning in Weather & Climate - Jupyter notebook exercises
This repository hosts the Jupyter notebook based exercises of the Massive Open Online Course (MOOC) on Machine Learning in Weather & Climate https://www.ecmwf.int/mlwc-mooc.
The notebook files can be found in the subdirectories corresponding to each tier of the MOOC. These include the following:
Tier 1 notebooks (ML in Weather & Climate)
In this tier there is only one notebook that demonstrates how to build a simple neural network on the WeatherBench dataset.
Tier 2 notebooks (Concepts of Machine Learning)
In this tier there are notebooks for each module that provide practical guidance on key concepts of Machine Learning.
Tier 3 notebooks (Practical ML Applications in Weather & Climate)
Each module of this tier contains notebooks that demonstrate practical applications of Machine Learning in the various stages of Numerical Weather and Climate prediction.
How to run the notebooks
The notebooks can either be downloaded and run on participants' own computers, or they can be run directly in various cloud environments. The advantage of the latter is that no software needs to be installed locally. In each notebook a number of options are provided where the notebook can be run. These may include the following:
| Colab | Kaggle | Deepnote |
|---|---|---|
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| Colab requires a Google account, which can easily be set-up for free. | Requires (free) registration with Kaggle. Once in, "switch on the internet" via settings. | Requires (free) registration. Deepnote is a good platform also for collaboration. |
License
Unless otherwise stated, the notebooks fall under Apache License 2.0. In applying this licence, ECMWF does not waive the privileges and immunities granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.