FUN MOOC: Gain practical understanding of the strengths and limitations of machine learning!

From Jan.17 to May 9, 2022, the FUN MOCC Platform will offer training on how to build predictive models with scikit-learn.  

 

Predictive modeling is a pillar of modern data science. In this field, scikit-learn is a central tool: it is easily accessible, yet powerful, and naturally dovetails in the wider ecosystem of data-science tools based on the Python programming language.

The course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science.

The course will teach you to be critical about each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions.

The training will be essentially practical, focusing on examples of applications with code executed by the participants.

This MOOC is completely free of charge. All the course materials are also available on a github repository.

The authors of the course are scikit-learn core developers, they will be your guides throughout the training!

Information Subscription, etc. 

https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/

Machine learning and Python in GMES and Africa

In GMES and Africa, the acquisition of data and products is partly handled by an Environmental Station (eStation) that was designed, developed, and is managed, by the European Commission Joint Research Center (JRC). To date, thanks to the GMES and Africa Support Programme and its precursors (AMESD, MESA), around 140 eStations were delivered to a similar amount of African institutions.  

The eStation2 is built upon open-source technologies. With on the backend PostgreSQL, Python, MapServer, GDAL/OGR, Graph Processing Tool (part of Sentinel’s Application Platform - SNAP), Ruffus and Apache web server. In the frontend the JavaScript frameworks OpenLayers, Ext JS and Highcharts. The data is acquired mainly from the SPOT/PROBAV, SEVIRI/MSG, TERRA-AQUA/M

More about the eStations, https://estation.jrc.ec.europa.eu/overview