Introduction to Machine Learning for Earth Observation
About the course
Introduction to Machine Learning course for NCEO members, which includes a range of accessible, online teaching resources aimed at helping you gain the knowledge and skills required to understand, assess, and run different Machine Learning (ML) algorithms.
Using the written materials, informative short videos, datasets, and dedicated computing resources that have been generated and provided for this course, NCEO members will learn to:
- Access High Performance Computing (HPC) via the Massive Graphics Processing Unit Cluster for Earth Observation (MAGEO)
- Load, process, and visualise optical satellite data using existing Jupyter Notebooks (Python)
- Trial and compare the running of applications on the Central Processing Unit (CPU) and the Graphics Processing Unit (GPU), and why it may become important to your future work to have access to a GPU cluster for Machine Learning and Artificial Intelligence
- Select and run machine learning models of increasing complexity, from Random Forest for classification to Neural Networks for Regression, to U-Net for Convolutional Neural Network classification
For general enquiries on this course
NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)
Plymouth Marine Laboratory
Prospect Place
Plymouth
PL1 3DH
Introduction and finding the course materials
Watch the four videos in the Introduction to Machine Learning for Earth Observation Playlist to learn more about what you will learn and where to find the course materials.
Accessing course materials
This online training course is entirely virtual and has been designed to be completed within a 1-week period, at your own pace, and without the addition of online or in-person support. NCEO members should contact the NCEO Training Officer to access the course materials and include details of supervisor, PI and/or NCEO institution.