Spatial Machine Learning and Statistics in Python

In this course, globally recognized expert Milan Janosov provides a hands-on introduction to the intersection of machine learning and spatial analytics, covering core concepts, challenges, and real-world applications. Learn about spatial statistics fundamentals, including spatial autocorrelation and interpolation, using Python libraries like GeoPandas. Dive into unsupervised machine learning techniques, such as hotspot analysis, K-Means, and DBSCAN clustering, and apply these techniques to geospatial data. Explore supervised learning methods, including spatial feature engineering, regression models, and spatial random forests for predictive analytics. Gain new insights into GeoAI and the future of spatial machine learning, setting the stage for further exploration. After completing this course, you will be able to conduct advanced statistical analysis and execute machine learning tasks on spatial data.
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