<p>In this course, you’ll work with real-world spatial data from Peninsular Malaysia to gain hands-on experience with mapping habitat suitability in conjunction with classical SDM models, such as MaxENT and Bioclim, and machine learning alternatives, such as random forests. The course will ensure that you are equipped to put spatial data and machine learning analysis into practice right away. You’ll have developed the skills necessary for working with ecological data and impress  potential employers with your GIS and machine learning skills in R. </p><p>Throughout the course, you’ll learn how to map suitable habitats for species using R. You’ll also explore common ecological modeling techniques and species distribution modeling (SDM) using real-life data. As you advance, the course will guide you in implementing some of the common Geographic Information Systems (GIS) and spatial data analysis techniques in R and use it to access ecological data.  You’ll perform common GIS and data analysis tasks related to SDMs, including accessing species-presence data, and get to grips with classical SDM techniques. </p><p> </p><p>All the code and supporting files are available at https://github.com/sanjanapackt/PacktPublishing-Species-Distribution-Models-with-GIS-and-Machine-Learning-in-R </p><p></p>