In this block lead by Tom Hengl and Leandro Parente (OpenGeoHub) participants learn how to use state-of-the-art Machine Learning algorithms in R (mlr, mlr3) for the purpose of building models and producing spatial and spatiotemporal predictions. We used some of the disease datasets and covariate layers (MOOD study area) mentioned in the previous sections, then show step-by-step how to run spatial spatiotemporal overlays, optimize models, run model diagnostics, produce and visualize predictions (as maps or animations). The block is based on the R bookdown: https://opengeohub.github.io/spatial-prediction-eml/