Machine Learning in R: Image Classification for LULC mapping

Learn supervised machine learning 4 Remote Sensing R & R-Studio, image classification, land use and land cover mapping

What you'll learn
Learn supervised machine learning for image classification using R-programming language in R-Studio
Learn theoretical background of Machine Learning
Apply machine learning based algorithms (random forest, SVM) for image classification analysis in R and R-Studio
Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning
Fully understand the basics of Land use and Land Cover (LULC) Mapping based on satellite image classification
Get an introduction and fully understand to Remote Sensing relevant for LULC mapping
Pre-process and analyze Remote Sensing images in R
Learn how to create training and validation data for image classification in QGIS
Build machine learning based image classification models for LUCL analysis and test their robustness in R
Implement Machine Learning algorithms, such as Random Forests, SVM in R
Apply accuracy assessment for Machine Learning based image classification in R
You'll have a copy of the scripts and step-by-step manuals used in the course for your reference to use in your analysis.

Description
Welcome to my unique course on Udemy on Machine Learning in R and R-Studio: Image classification for land use and land cover (LULC) mapping!

This is the first course on Udemy that offers a possibility to learn much-wanted skills of R programming for RS-based Machine Learning analysis in R.

Why geospatial analysts (GIS, Remote Sensing) should learn R?

With the knowledge gained in this course, you will be ready to undertake your first very own Machine Learning image data analysis in R. Oracle estimated over 2 million R users worldwide, cementing R as a leading programming language in statistics and data science. Every year, the number of R users grows by about 40%, and an increasing number of organizations are using it in their day-to-day activities. Begin your journey to learn R with us today to future proof your career tomorrow!

THIS COURSE HAS 7 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTICE

Learn the theoretical background of Machine Learning

Learn supervised machine learning for image classification

Apply machine learning-based algorithms (random forest, SVM) for image classification analysis in R and R-Studio

Learn the basics of R-programming

Fully understand the basics of Land use and Land Cover (LULC) Mapping based on satellite image classification

Understand Remote Sensing basics relevant for LULC mapping

Learn how to create training and validation data for image classification in QGIS

Build machine learning-based image classification models for LUCL analysis and test their robustness in R

Apply accuracy assessment for Machine Learning based image classification in R

NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:

You’ll start by absorbing the most valuable Machine Learning basics, and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R for the case of satellite image analysis.

My course will help you implement the methods using real image data obtained from different sources (Landsat and Sentinel images). Thus, after completing my Machine Learning course in R for image classification and LULC analysis, you’ll easily use different data streams and data science packages to work with real data in R.

In case it is your first encounter with R, don’t worry, my course is a full introduction to R & R programming in this course.

This course is different from other training resources. Each lecture seeks to enhance your GIS and Remote Sensing skills and R in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing spatial data for your own projects and gain appreciation from your future employers with your advanced GIS skills and knowledge of cutting edge machine learning algorithms and R programming.

One important part of the course is the practical exercises. You will be given some precise instructions, scripts and datasets to run Machine Learning algorithms using the R tools.