Last Updated on October 1, 2023 by GeeksGod
Course : Species Distribution Models with GIS & Machine Learning in R
Free Udemy Coupon and Species Distribution Models
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 world 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 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 learn 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, the 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 inside the course.
Species Distribution Models and Free Udemy Coupon
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 world 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 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 learn 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, the 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 inside the course.
Hurry up! Enroll in the course and don’t forget to use the Free Udemy Coupon available for a limited time. Start your journey to becoming an expert in species distribution models and unlock exciting opportunities in the field of ecology and conservation.
Don’t miss out on this incredible opportunity to learn GIS, machine learning, and species distribution models. Enroll now!
Need more reasons to enroll?
- Gain practical experience in implementing machine learning algorithms in R.
- Learn how to access and analyze real-life spatial data for ecological modeling.
- Enhance your resume with valuable skills in GIS and machine learning.
- Impress potential employers with your expertise in species distribution modeling.
- Access the course materials at your own pace and from anywhere.
Enroll today and take the first step towards becoming a skilled ecologist or conservationist in the field of GIS and machine learning. Remember, the Free Udemy Coupon is available for a limited time, so don’t miss out on this opportunity.
Still unsure? Here’s what our students say:
“This course exceeded my expectations. The instructor’s knowledge and expertise in the field of ecological modeling and GIS is commendable. I learned a lot and feel confident in implementing species distribution models in R. Highly recommend!” – John D.
“The content of this course is invaluable. I have been looking for a comprehensive course on GIS and machine learning for ecological modeling, and this course delivered everything I needed. The hands-on projects were particularly helpful in solidifying my understanding of the concepts. Thank you!” – Sarah L.
Do you want to join hundreds of satisfied students who have successfully learned GIS, machine learning, and species distribution models? Enroll now and start your learning journey today!
