Species Distribution Models with GIS & Machine Learning in R from Udemy

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 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.

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.

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Teaches students how to harness the power of both GIS and Machine Learning in R for ecological modelling

Course is taught by an experienced PhD in the field who has several years of experience in analyzing spatial data

Focuses on practical, hands-on learning with real-world examples and data

Covers both classical and machine learning techniques for species distribution modelling

Suitable for ecologists, conservationists, and anyone interested in using GIS and Machine Learning for ecological modelling

Career center

Learners who complete Species Distribution Models with GIS & Machine Learning in R will develop knowledge and skills that may be useful to these careers:

Ecological Modeler

Ecological modelers use mathematical and computational techniques to simulate and predict the behavior of ecological systems. This course provides a strong foundation for this field by teaching the skills necessary to implement species distribution modelling and to use machine learning techniques in R. These skills are essential for ecological modelers who need to develop models that can predict the distribution and abundance of species.

See salaries and explore the career path for Ecological Modeler

Conservation Scientist

Conservation science is a discipline that applies ecological principles to the conservation and restoration of Earth's ecosystems. This course provides a strong foundation for this field by teaching the skills necessary to map suitable habitats for species and to implement practical machine learning models in R. These skills are essential for conservation scientists who need to understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Conservation Scientist

Habitat Suitability Modeler

Habitat suitability modelers use ecological data to develop models that can predict the suitability of different habitats for a particular species. This course provides a strong foundation for this field by teaching the skills necessary to implement species distribution modelling and to use machine learning techniques in R. These skills are essential for habitat suitability modelers who need to develop models that can help decision-makers identify and protect suitable habitats for species.

See salaries and explore the career path for Habitat Suitability Modeler

Spatial Analyst

Spatial analysts use geographic information systems (GIS) to analyze and visualize spatial data. This course provides a strong foundation for this field by teaching the skills necessary to use GIS for species distribution modelling. These skills are essential for spatial analysts who need to develop maps and other visualizations that can help decision-makers understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Spatial Analyst

Environmental Consultant

Environmental consultants provide advice to businesses and governments on how to minimize their environmental impact. This course provides a strong foundation for this field by teaching the skills necessary to map suitable habitats for species and to implement practical machine learning models in R. These skills are essential for environmental consultants who need to understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Environmental Consultant

Wildlife Biologist

Wildlife biologists study the behavior, ecology, and conservation of wildlife. This course provides a strong foundation for this field by teaching the skills necessary to map suitable habitats for species and to implement practical machine learning models in R. These skills are essential for wildlife biologists who need to understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Wildlife Biologist

Natural Resource Manager

Natural resource managers develop and implement plans to manage natural resources, such as forests, water, and wildlife. This course provides a strong foundation for this field by teaching the skills necessary to map suitable habitats for species and to implement practical machine learning models in R. These skills are essential for natural resource managers who need to understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Natural Resource Manager

GIS Analyst

GIS analysts use geographic information systems (GIS) to analyze and visualize spatial data. This course provides a strong foundation for this field by teaching the skills necessary to use GIS for species distribution modelling. These skills are essential for GIS analysts who need to develop maps and other visualizations that can help decision-makers understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for GIS Analyst

Zoologist

Zoologists study the behavior, ecology, and evolution of animals. This course provides a strong foundation for this field by teaching the skills necessary to map suitable habitats for species and to implement practical machine learning models in R. These skills are essential for zoologists who need to understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Zoologist

Research Scientist

Research scientists conduct research to advance scientific knowledge. This course provides a strong foundation for this field by teaching the skills necessary to implement species distribution modelling and to use machine learning techniques in R. These skills are essential for research scientists who need to develop models that can be used to predict the distribution and abundance of species.

See salaries and explore the career path for Research Scientist

Machine Learning Engineer

Machine learning engineers develop and implement machine learning models to solve a variety of problems. This course provides a strong foundation for this field by teaching the skills necessary to implement practical machine learning models in R. These skills are essential for machine learning engineers who need to develop models that can be used to predict the distribution and abundance of species.

See salaries and explore the career path for Machine Learning Engineer

Remote Sensing Analyst

Remote sensing analysts use satellite imagery and other data to collect information about the Earth's surface. This course provides a strong foundation for this field by teaching the skills necessary to use GIS for species distribution modelling. These skills are essential for remote sensing analysts who need to develop maps and other visualizations that can help decision-makers understand the distribution of species and to develop strategies to protect them.

See salaries and explore the career path for Remote Sensing Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide insights into a variety of problems. This course provides a strong foundation for this field by teaching the skills necessary to implement species distribution modelling and to use machine learning techniques in R. These skills are essential for statisticians who need to develop models that can be used to predict the distribution and abundance of species.

See salaries and explore the career path for Statistician

Data Analyst

Data analysts collect, clean, and analyze data to provide insights into a variety of problems. This course may be useful for data analysts who want to learn more about species distribution modelling and machine learning techniques in R.

See salaries and explore the career path for Data Analyst

Software Engineer

Software engineers design, develop, and maintain software applications. This course may be useful for software engineers who want to learn more about machine learning techniques in R.

See salaries and explore the career path for Software Engineer

Species Distribution Models with GIS & Machine Learning in R

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