This course provides an in-depth understanding of machine learning algorithms and techniques for geospatial data analysis. The course covers the fundamentals of machine learning and its application in geospatial data analysis, including feature extraction, data preprocessing, and model selection. It also covers various machine learning algorithms, such as random forest and neural networks, and their application in geospatial data analysis. The course provides hands-on experience with real-world geospatial datasets and tools, such as Python and its relevant libraries, and Google Earth Engine. Upon completion of the course, students will have the skills and knowledge to apply machine learning algorithms to geospatial data analysis and solve real-world problems.

Assessment: 100% coursework