Getting Digital

Species Distribution Models with GIS & Machine Learning in R

Develop essential life sciences skills with expert instruction and practical examples.

Online Course
Self-paced learning
Flexible Schedule
Learn at your pace
Expert Instructor
Industry professional
Certificate
Upon completion
What You'll Learn
Master the fundamentals of life sciences
Apply best practices and industry standards
Build practical projects to demonstrate your skills
Understand advanced concepts and techniques

Skills you'll gain:

Professional SkillsBest PracticesIndustry Standards
Prerequisites & Target Audience

Skill Level

IntermediateSome prior knowledge recommended

Requirements

Basic understanding of life sciences
Enthusiasm to learn
Access to necessary software/tools
Commitment to practice

Who This Course Is For

Professionals working in life sciences
Students and career changers
Freelancers and consultants
Anyone looking to improve their skills
Course Information

About This Course

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.

Provider
Udemy
Estimated Duration
10-20 hours
Language
English
Category
Science & Academia

Topics Covered

Life SciencesMachine Learning

Course Details

Format
Online, Self-Paced
Access
Lifetime
Certificate
Upon Completion
Support
Q&A Forum
Course Details
Ready to get started?

View pricing and check out the reviews. See what other learners had to say about the course.

Get started and enroll now
Money-back guarantee might be available
Join thousands of students

This course includes:

Lifetime access to course content
Access on mobile and desktop
Certificate of completion
Downloadable resources

Not sure if this is right for you?

Browse More Life Sciences Courses

Continue Your Learning Journey

Explore more Life Sciences courses to deepen your skills and advance your expertise.

Studying of bacterial shape, cell wall, size, …. & so on, need to scope the bacteria under microscope. But you can not s...
An Introduction to the study of bacteria, viruses, fungi, and protozoa, with a main emphasis on bacteria. Topics include...
Do you have numerous tender points (especially between the shoulder blades), sore muscles, sore fascia and connective ti...
Welcome to Part I of The General Biology Course series here on Udemy.Biology is the study of life. In this course you wi...
Carbohydrates are one of the main classes of nutrients that provide energy to the body. This course will cover the funda...