Introduction to AI offers a comprehensive journey into the world of AI, starting from its foundational concepts to its practical applications across various industries. This Mini-MOOC is designed to demystify AI and make it accessible to learners with varying levels of expertise.
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This course aims to enhance participants’ competencies in understanding cloud computing fundamentals, navigating major cloud providers, cloud storage management, ensuring cloud security, and applying practical solutions to real-world business challenges.
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By the end of this mini MOOC, student will understand the concept, value, and social and environmental significance of ecosystem services (ES), recognising their importance for environmental management. Furthermore, they will be able to evaluate landscapes, accurately depicting the impacts of human activities upon them. Additionally, the student will have skills in developing ideas for
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The student is able to comprehend information about sustainable development and has the ability to apply the concepts of sustainable development for reaching the Sustainable Development Goals in all personal and professional activities.
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This Mini-MOOC is relevant information for communicating with local and regional planners of civil servants and the general public as well related to any SDG that includes a spatial dimension from global to local; for the sustainable use of land, adaption and mitigation actions for risks like flooding, soil degradation, ecosystem instabilities and conservation of ecosystems
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QGIS is one of the most used desktop software for geospatial data management, analysis and visualisation. This entity provides the basics for using QGIS for the first time, or refreshing memory of the software interface and functionalities.
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The objective of this course is to learn the key elements on research and data collection planning. You will be able to evaluate the data collection process and recognize the role of documentation on field data collection. The goal is to learn how to plan and create a comprehensive data collection project on QGIS and
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Implementing land cover/land use (LCLU) classification based on Earth observation data and ML/AI classifiers needs a substantial amount of LCLU samples covering the study area.
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The goal of this nugget is to introduce you to different methods and techniques for detecting and analysing LCLU changes using EO time-series data in Google Earth Engine (GEE).
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This module introduces students to citizen science data for Earth Observation, focusing on handling crowdsourced land cover and land use (LCLU) data samples in the R program. It covers the statistical accuracy assessment of citizen science data and the preparation of Collect Earth Online (CEO) LCLU samples data for satellite image classification.
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