Harnessing AI and Machine Learning for Geospatial Analysis
Python and R for satellite data - machine learning, deep learning, Google Earth Engine and real case studies
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Harnessing AI and Machine Learning for Geospatial Analysis is a Development course on Udemy. Python and R for satellite data - machine learning, deep learning, Google Earth Engine and real case studies The material is organised so learners can progress from the fundamentals to applying what they have learned in realistic scenarios.
Key Skills You Will Learn
- Work with geospatial data in both Python and R - importing, manipulating, visualising and exporting spatial datasets
- Calculate remote sensing indices and run zonal statistics on satellite imagery in Python
- Build and evaluate machine learning models for geospatial problems such as crop health and land classification
- Train deep learning models: neural networks in R and a convolutional neural network for image classification in PyTorch
- Use Google Earth Engine to improve crop classification accuracy on large satellite datasets
- Detect and count plants automatically using computer vision techniques
Who This Course Is For
This course suits learners who want a guided, self-paced path into Development, from complete beginners to practitioners looking to refresh or formalise their knowledge. Check the curriculum and requirements section on Udemy to confirm it matches your current level.
Our Take
If the topic aligns with your goals, this is a convenient way to build skills on your own schedule. Udemy courses include lifetime access to the enrolled content, so you can revisit lessons whenever you need them.
Before you enroll
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- Make sure Udemy shows Free (or $0) at checkout before you complete enrollment.
- Once you enroll with a valid coupon, the course stays in your library with lifetime access.
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