Species Distribution Models with GIS & Machine Learning in R

Species Distribution Models with GIS & Machine Learning in R, available at $59.99, has an average rating of 4.44, with 45 lectures, based on 571 reviews, and has 8956 subscribers.

You will learn about You will have a greater clarity of basic spatial data concepts and data types Carry out practical spatial data analysis tasks in freely available software in R Analyze spatial data using R This course is ideal for individuals who are Academics or Researchers or Conservation managers or Anybody who works/will work with spatial data It is particularly useful for Academics or Researchers or Conservation managers or Anybody who works/will work with spatial data.

Enroll now: Species Distribution Models with GIS & Machine Learning in R

Summary

Title: Species Distribution Models with GIS & Machine Learning in R

Price: $59.99

Average Rating: 4.44

Number of Lectures: 45

Number of Published Lectures: 43

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 43

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

You will have a greater clarity of basic spatial data concepts and data types
Carry out practical spatial data analysis tasks in freely available software in R
Analyze spatial data using R

Who Should Attend

Academics
Researchers
Conservation managers
Anybody who works/will work with spatial data

Target Audiences

Academics
Researchers
Conservation managers
Anybody who works/will work with spatial data

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.

Course Curriculum

Chapter 1: Introduction to the Species Distribution Modelling Course

Lecture 1: INTRODUCTION TO THE COURSE: Instructor & Course Details

Lecture 2: What is Species Distribution Modelling?

Lecture 3: Data used in the course

Lecture 4: Introduction to R for habitat suitability modelling

Lecture 5: Conclusion to Section 1

Chapter 2: The Basics of GIS for Species Distribution Models (SDMs)-Part 1

Lecture 1: Where to Obtain Raster Data for Building SDMs

Lecture 2: Accessing and Cleaning GBIF Data

Lecture 3: Other Sources of Species Geo-location Data

Lecture 4: Extract Species Geo-location Data from Other Sources in R

Lecture 5: Access Climate & Other Data via R

Lecture 6: Working With Elevation Data in R

Lecture 7: Deriving Topographic Products from Elevation Data

Lecture 8: Conclusions to Section 2

Chapter 3: Pre-Processing Raster and Spatial Data for SDMs

Lecture 1: Some Prerequisites

Lecture 2: CRS of the Data

Lecture 3: Clip Raster Data to a Given Extent

Lecture 4: Resize the Raster Data

Lecture 5: Basic Data Visualization

Lecture 6: Conclusions to Section 3

Chapter 4: Classical SDM Techniques

Lecture 1: Underlying Rationale

Lecture 2: Bioclim

Lecture 3: Model Evaluation

Lecture 4: Maxent Interface in R

Lecture 5: Maxent SDM in R

Lecture 6: Maxent Analysis with the red package

Lecture 7: Domain SDM in R

Lecture 8: Conclusion to Section 4

Chapter 5: Machine Learning Models for Habitat Suitability

Lecture 1: Machine Learning Modelling

Lecture 2: Pre-processing Steps Prior to Modelling With Presence & Absence Data

Lecture 3: Prior to Implementing Machine Learning

Lecture 4: GLMs for Habitat Suitability

Lecture 5: Support Vector Machines

Lecture 6: kNN

Lecture 7: Random Forest (RF)

Lecture 8: Gradient Boosting Machine (GBM)

Lecture 9: Further Model Evaluation

Lecture 10: Conclusions to Section 5

Chapter 6: Extra Lectures

Lecture 1: Obtain Elevation Data rom Within R

Lecture 2: Evaluate Point Density

Lecture 3: Introduction to Leaflet

Lecture 4: Github

Lecture 5: Brazil Time Lapse

Lecture 6: Assign Legends in QGIS

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 12 votes
2 stars: 30 votes
3 stars: 72 votes
4 stars: 154 votes
5 stars: 303 votes

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