Species Distribution Models with GIS & Machine Learning in R
Why take this course?
🌱 Embark on Your Journey into Species Distribution Models with GIS & Machine Learning in R!
🚀 Are You an Ecologist or Conservationist Interested in Mastering GIS and Machine Learning in R? 🍃💻
- Are you eager to conduct habitat suitability mapping as a ecologist/conservationist?
- Are you looking for your first steps into using R for ecological data and GIS analysis?
- Do you aspire to implement practical machine learning models in R for conservation efforts?
Then this course is perfect for you! 🎓🎉
Meet your course instructor, Minerva Singh, an Oxford University MPhil (Geography and Environment) graduate and a PhD from Cambridge University (Tropical Ecology and Conservation). With years of experience in analyzing real-life spatial data, Minerva has published in international peer-reviewed journals. She's here to guide you through the complexities of species distribution modeling using GIS & Machine Learning in R.
This course is your chance to work with actual spatial data from Peninsular Malaysia to map suitable habitats for species. You'll learn to integrate classical SDM models like MaxEnt and machine learning alternatives such as Random Forests into your conservation projects. The goal? To enable you to put your skills into practice today, impress potential employers with concrete examples of your GIS and Machine Learning abilities in R, and make a tangible impact on ecological conservation.
Why Choose This Course? 🤔✨
- Real-Life Application: Gain hands-on experience using real ecological data for habitat suitability mapping.
- Cutting-Edge Techniques: Access the most common machine learning algorithms in R and learn to apply them to ecological data.
- Expert Guidance: Follow Minerva's lead as she walks you through the process of implementing SDMs using GIS techniques and machine learning models in R.
- No Complex Math: Learn in a simple, engaging manner, even if you're not a mathematician or a statistician!
What You Will Learn: 📚
- Introduction to SDMs & Habitat Suitability Mapping
- GIS for Species Distribution Models (SDMs): Master the basics of GIS and data analysis tasks related to SDMs, including accessing species presence data via R.
- Pre-Processing Spatial Data for SDMs in R: Learn to perform common GIS techniques on raster and spatial data.
- Classical SDM Techniques: Understand classical models like MaxENT and Bioclim and how to implement them in R.
- Machine Learning Models for Habitat Suitability: Apply and interpret common machine learning techniques using the birds of Peninsular Malaysia as a case study.
This course is practical and hands-on, focusing on applying new concepts and techniques learned to your own projects after each video. You'll see immediate results, making your experience both enlightening and impactful. 🔍🌳📈
Take Action Today! 🚀
Join Minerva in this engaging course and get personal support every step of the way. With Udemy's 30-day Money Back Guarantee, you have nothing to lose and everything to gain. Enroll now and let's embark on this exciting journey together! 🤝💫
Enroll Now and transform your ecological conservation efforts with the power of GIS & Machine Learning in R.
Course Gallery
Loading charts...
Comidoc Review
Our Verdict
Species Distribution Models with GIS & Machine Learning in R offers valuable insights into mapping habitat suitability for conservation using data analysis tools. Its comprehensive approach covers various aspects of species distribution and machine learning methods, although minor inconsistencies and outdated references require some additional effort from learners. Overall, this dynamic course can serve as a great introduction to the field for both beginners and R users looking for practical real-life applications within environmental sustainability.
What We Liked
- Covers a wide range of topics and methodologies related to species distribution models (SDMs) using GIS and machine learning in R
- Provides clear explanations and examples, making it suitable for both beginners and those with some knowledge of R or SDMs
- Includes additional resources such as extra lessons and an active Q&A section, providing further learning opportunities
- Lectures are engaging and informative, focusing on practical applications in the field of environmental sustainability
Potential Drawbacks
- Some instructions and scripts contain outdated information, referencing packages or files no longer available
- Occasional errors may occur while running R scripts, requiring manual troubleshooting or package installation
- The presentation could be improved in terms of slide design and narration being monotonous in some parts
- For beginners, minor inconsistencies in the code might pose challenges despite the overall clear explanations