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Updated in [August 21st, 2023]

What skills and knowledge will you acquire during this course?
By taking this course, you will acquire the skills and knowledge to:

- Carry out habitat suitability mapping
- Access ecological data and perform GIS analysis using R
- Implement practical machine learning models in R
- Implement species distribution modelling and map suitable habitats for species in R
- Analyze real-life spatial data from different sources
- Produce publications for international peer-reviewed journals
- Work with actual spatial data from Peninsular Malaysia for mapping habitat suitability
- Implement classical SDM models like MaxEnt and machine learning alternatives such as Random Forests
- Put spatial data and machine learning analysis into practice
- Start ecological data for your own projects
- Impress potential employers with examples of your GIS and Machine Learning skills in R
- Gain exposure to common Geographic Information Systems (GIS) and spatial data analysis techniques in R
- Access ecological data via R
- Harness the power of GIS and Machine Learning in R for ecological modelling
- Learn the state of the art in Machine learning in a simple and fun way without complex math or boring explanations
- Apply practical machine learning techniques in R to real data
- Interpret the results of different techniques applied to real data
- Receive personal support from the instructor to ensure a successful learning experience
- Take advantage of Udemy's 30-day Money Back Refund Policy if unsatisfied with the course.

How does this course contribute to professional growth?
This course on Species Distribution Models with GIS & Machine Learning in R contributes to professional growth by providing ecologists and conservationists with the skills and knowledge needed to carry out habitat suitability mapping, access ecological data, and perform GIS analysis using R. By learning how to implement species distribution modeling and map suitable habitats for species, participants will gain practical experience in using machine learning models and GIS techniques for ecological modeling. This course also offers exposure to working with real-life spatial data and implementing common machine learning algorithms on ecological data in R. Overall, completing this course will enhance participants' GIS and machine learning skills, making them more competitive in the field and impressing potential employers with their practical examples of GIS and machine learning abilities.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education in the field of ecology and conservation. It covers topics such as habitat suitability mapping, accessing ecological data, GIS analysis, and implementing machine learning models in R. The course is designed for individuals who want to learn the state of the art in machine learning and GIS in a practical and hands-on way.

Course Syllabus

Introduction to the Species Distribution Modelling Course

The Basics of GIS for Species Distribution Models (SDMs)-Part 1

Pre-Processing Raster and Spatial Data for SDMs

Classical SDM Techniques

Machine Learning Models for Habitat Suitability

Extra Lectures