Machine Learning with Remote Sensing Data
- 4.1
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Brief Introduction
Learn to apply machine learning, remote sensing, big spatial data using the Google Earth Engine cloud computingDescription
Do you want to learn how to access, process and analyze remote sensing data using open source cloud-based platforms?
Do you want to master machine learning algorithms to predict Earth Observation big data?
Do you want to start a spatial data scientist career in the geospatial industry?
Enroll in my new course to master Machine Learning with Remote Sensing Data.
I will provide you with hands-on training with example data, sample scripts, and real-world applications.
By taking this course, you will take your geospatial data science skills to the next level by gaining proficiency in applying machine learning algorithms to predict satellite data using an open source big data analytics tool, Earth Engine API, a cloud-based Earth observation data visualization analysis by powered by Google.
What makes me qualified to teach you?
I am Dr. Alemayehu Midekisa, PhD. I am a geospatial data scientist, instructor and author. I have over 15 plus years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM and other remote sensing products.
In this Machine Learning with Earth Engine API course, I will help you get up and running on the Google Earth Engine cloud platform. Then you will apply various machine learning algorithms including linear regression, clustering, CART, and random forests. We will use Landsat satellite data to predict land use land cover classification. All sample data and script will be provided to you as an added bonus throughout the course.
Jump in right now to enroll. To get started click the enroll button.
Requirements
- Requirements
- Basic knowledge of satellite remote sensing is needed.
- No prior knowledge on machine learning is needed.
Spatial eLearning
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