Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R?
- Are you an ecologist/conservationist looking to carry out habitat suitability mapping?
- Are you an ecologist/conservationist looking to get started with R for accessing ecological data and GIS analysis?
- Do you want to implement practical machine learning models in R?
Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R.
My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.
In this course, actual spatial data from Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests. The underlying motivation for the course is to ensure you can put spatial data and machine learning analysis into practice today. Start ecological data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your GIS and Machine Learning skills in R.
So Many R based Machine Learning and GIS Courses Out There, Why This One?
This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real ecological data in R. Plus, you will gain exposure to working your way through a common ecological modelling technique- species distribution modelling (SDM) using real life data. Students will also gain exposure to implementing some of the most common Geographic Information Systems (GIS) and spatial data analysis techniques in R. Additionally, students will learn how to access ecological data via R.
You will learn to harness the power of both GIS and Machine Learning in R for ecological modelling.
I have designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Yes, even non-ecologists can get started with practical machine learning techniques in R while working their way through real data.
What you will Learn in this Course
This is how the course is structured:
- Introduction – Introduction to SDMs and mapping habitat suitability
- The Basics of GIS for Species Distribution Models (SDMs) – You will learn some of the most common GIS and data analysis tasks related to SDMs including accessing species presence data via R
- Pre-Processing Raster and Spatial Data for SDMs - Your R based GIS training and will continue and you will earn to perform some of the most common GIS techniques on raster and other spatial data
- Classical SDM Techniques - Introduction to the classical models and their implementation in R (MaxENT and Bioclim)
- Machine Learning Models for Habitat Suitability - Implement and interpret common ML techniques to build habitat suitability maps for the birds of Peninsular Malaysia.
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts . However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.