Machine Learning in R: Land Use Land Cover Image Analysis from Udemy

Mastering Machine Learning in R and R-Studio: Image Classification for Land Use and Land Cover (LULC) Mapping

Welcome to this unique Udemy course on Machine Learning in R and R-Studio, focusing on image classification for land use and land cover (LULC) mapping.

Why Should Geospatial Analysts (GIS, Remote Sensing) Learn R?

This course is a pioneering offering on Udemy, providing you with the opportunity to acquire highly sought-after R programming skills for Remote Sensing-based Machine Learning analysis in R.

The knowledge you gain in this course will empower you to embark on your own Machine Learning image data analysis in R. With over 2 million R users worldwide, Oracle has solidified R's position as a leading programming language in statistics and data science. The R user base grows by approximately 40% each year, and an increasing number of organizations rely on it for their day-to-day operations. By enrolling in this course today, you are taking a proactive step to future-proof your career.

Course Highlights:

This comprehensive course comprises 7 sections, meticulously covering every aspect of Machine Learning, encompassing both theory and practice. You will:

Gain a solid theoretical foundation in Machine Learning.
Master supervised machine learning techniques for image classification.
Apply machine learning algorithms (such as random forest and SVM) for image classification analysis in R and R-Studio.
Acquire a fundamental understanding of R programming.
Fully grasp the basics of Land Use and Land Cover (LULC) Mapping based on satellite image classification.
Comprehend the fundamentals of Remote Sensing pertinent to LULC mapping.
Learn how to create training and validation datasets for image classification in QGIS.
Build machine learning-based image classification models for LULC analysis and evaluate their robustness in R.
Apply accuracy assessment to Machine Learning-based image classification in R.

No Prior R or Statistics/Machine Learning/R Knowledge Required:

This course begins with a comprehensive introduction to the most essential Machine Learning concepts and techniques. I employ easy-to-follow, hands-on methods to demystify even the most intricate R programming concepts, especially in the context of satellite image analysis.

Throughout the course, you will implement these techniques using real image data sourced from various providers, including Landsat and Sentinel images. As a result, upon completion of this Machine Learning course in R for image classification and LULC analysis, you will possess the skills to work with diverse data streams and data science packages to analyze real data in R.

If this is your initial encounter with R, rest assured. This course serves as a comprehensive introduction to R and R programming.

What Sets This Course Apart?

This course distinguishes itself from other training resources by delivering practical, hands-on solutions in an easy-to-follow manner, aimed at enhancing your GIS and Remote Sensing skills, as well as your proficiency in R. You will be equipped to initiate spatial data analysis for your own projects, earning recognition from future employers for your advanced GIS capabilities, mastery of cutting-edge machine learning algorithms, and R programming proficiency.

Integral to the course are practical exercises. You will receive precise instructions, scripts, and datasets to execute Machine Learning algorithms using R tools.

Join This Course Now and Elevate Your Expertise.

What's inside

Learning objectives

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.
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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.
Show more
Show less

Syllabus

Introduction

What is R and RStudio?

How to install R and RStudio in 2021

Lab: Install R and RStudio in 2021

Lab: Installing QGIS and install SCP

A note on QGIS versions and it's plug-ins

to master machine Learning in R: Land Use/Land Cover Image Analysis with satellite images

Introduction to Machine Learning

Basics of machine learning for classification analysis

Common algorithms of image classification

Software used in this course R-Studio and Introduction to R

Lab: Introduction to RStudio Interface

Lab: Installing Packages and Package Management in R

Variables in R and assigning Variables in R

Lab: Variables in R and assigning Variables in R

Overview of data types and data structures in R

Lab: data types and data structures in R

Vectors' operations in R

Data types and data structures: Factors

Dataframes: overview in R

Functions in R - overview

For Loops in R

Read Data into R

To learn basics of Remote Sensing for LULC mapping

Introduction to digital image

Sensors and Platforms

Understanding Remote Sensing for LULC mapping

Stages of LULC supervised classification

Satellite image preparation in R for Land use / land cover (LULC) analysis in R

Data used for analysis: Landsat images

Preprocessing of satellite image data

Overview of processing steps in R for Landsat images

Lab: Image load in R

Lab: Image Layerstacks in R

Lab: Batch Processing in R: unzipp, laerstack of LAndsat images

Visualize images in R

To learn how to prepare the training data in R in raster package

Data used for analysis: Sentinel images

Training data requirements for classification and training data selection

Lab: Prepare training data in R - part 1

Lab: Prepare training data in R - part 2

Plotting spectral signatures in R

To perform Land Use/Land Cover Image Classification in R using Machine Learning algorithms

Image Classification in R with Random Forest in R

Map visualization: Creating classified image based on Random Forest model in R

Map visualization: Create a classified image based on RF model in QGIS

Image Classification in R with Support Vector Machines (SVM) in R

Accuracy assessment of image classification

Lab: Accuracy Assessment (validation) of classification in R

Independent Task: Accuracy assessment for SVM-based classification

Lab: Creating a LULC map of your final image classification result in QGIS

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches advanced topics that are typically reserved for more advanced GIS and remote sensing students

Focuses on LULC analysis and land cover mapping, which are essential skills for geospatial analysis and environmental monitoring

Provides hands-on experience with real-world datasets and industry-standard software, including R, R-Studio, QGIS, and SCP

Includes practical exercises and step-by-step manuals that make it accessible to students with varying levels of experience

Covers a comprehensive range of topics, including machine learning, image classification, and remote sensing, making it a valuable resource for students

Instructors have extensive experience in the field and are recognized for their contributions to geospatial analysis and remote sensing

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning in R: Land Use Land Cover Image Analysis with these activities:

Review R Programming Concepts

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Review the basics of R programming, including data types, data structures, and control flow.

Browse courses on R Programming

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Review online tutorials or documentation on R programming.
Complete coding exercises.
Work on small coding projects.

Create a Study Guide for Machine Learning for Image Classification

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Compile a study guide that summarizes the key concepts and techniques of machine learning for image classification.

Browse courses on Machine Learning

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Identify the key concepts and techniques of machine learning for image classification.
Summarize these concepts and techniques in a clear and concise manner.
Organize your study guide into sections and subsections.
Review your study guide regularly.

Follow Video Tutorials on Machine Learning for Image Classification

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Follow video tutorials that provide step-by-step guidance on how to use machine learning for image classification tasks.

Browse courses on Machine Learning

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Search for video tutorials on machine learning for image classification.
Choose a tutorial that matches your skill level.
Watch the tutorial and follow along with the instructions.
Complete the exercises in the tutorial.
Apply what you have learned to your own image classification projects.

Four other activities

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Practice Supervised Machine Learning Algorithms for Image Classification

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Practice using supervised machine learning algorithms, such as random forest and SVM, to perform image classification tasks.

Browse courses on Machine Learning

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Load training data into R.
Preprocess the data.
Create a supervised machine learning model.
Train the model on the training data.
Evaluate the model on the test data.

Read a Book on Machine Learning for Image Classification

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Read a comprehensive book on machine learning for image classification to gain a deeper understanding of the concepts and techniques involved.

View Deep Learning through Sparse and Low-Rank... on Amazon

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Choose a book that is appropriate for your skill level.
Read the book carefully.
Take notes and summarize the key points.
Complete the exercises in the book.
Apply what you have learned to your own image classification projects.

Write a Blog Post or Article on Machine Learning for Image Classification

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Write a blog post or article that summarizes the key concepts and techniques of machine learning for image classification.

Browse courses on Machine Learning

Show steps

Choose a topic for your blog post or article.
Research the topic and gather information.
Write a draft of your blog post or article.
Edit and revise your draft.
Publish your blog post or article.

Build an Image Classification Model for a Real-World Problem

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Build and deploy an image classification model that addresses a real-world problem, such as classifying medical images or detecting fraud.

Browse courses on Machine Learning

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Identify a real-world problem that can be solved using image classification.
Gather a dataset of images that represent the problem.
Preprocess the images and extract features.
Train an image classification model on the dataset.
Deploy the model and test its performance on new images.

Career center

Learners who complete Machine Learning in R: Land Use Land Cover Image Analysis will develop knowledge and skills that may be useful to these careers:

Remote Sensing Analyst

Remote Sensing Analysts specialize in extracting valuable information from satellite imagery and other remotely sensed data. This course is a great fit for aspiring Remote Sensing Analysts, as it provides a comprehensive foundation in Remote Sensing, as well as practical skills in image processing, classification, and accuracy assessment using R.

See salaries and explore the career path for Remote Sensing Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain Machine Learning models. This course will provide you with the skills you need to use R for Machine Learning, including supervised machine learning techniques, feature engineering, and model evaluation. This makes it an excellent choice for those aspiring to become Machine Learning Engineers who want to specialize in image classification tasks.

See salaries and explore the career path for Machine Learning Engineer

Geospatial Analyst

Geospatial Analysts use geospatial data to solve real-world problems in fields such as environmental science, urban planning, and natural resource management. By taking this course, you'll learn how to use R for geospatial data analysis, including Machine Learning for image classification, which will make you a highly sought-after Geospatial Analyst.

See salaries and explore the career path for Geospatial Analyst

Research Scientist

Research Scientists conduct research in a variety of fields, including environmental science, remote sensing, and geospatial analysis. This course will provide you with the skills you need to use Machine Learning for land use and land cover mapping, which can be used to conduct research on a wide range of topics, including climate change, land use change, and the impact of human activities on the environment.

See salaries and explore the career path for Research Scientist

Urban Planner

Urban Planners work to create livable and sustainable cities. This course will provide you with the skills you need to use R for urban planning, including Machine Learning for land use and land cover mapping. These skills will make you a more effective Urban Planner.

See salaries and explore the career path for Urban Planner

Data Analyst

Data Analysts use data to solve business problems. This course will provide you with the skills you need to use R for data analysis, including Machine Learning for image classification. These skills will make you a more valuable asset to any data analytics team.

See salaries and explore the career path for Data Analyst

Cartographer

Cartographers create maps and other visual representations of geographic data. This course will provide you with the skills you need to use R for cartography, including Machine Learning for land use and land cover mapping. These skills will make you a more effective Cartographer.

See salaries and explore the career path for Cartographer

Land Use Planner

Land Use Planners work to ensure that land is used in a sustainable and efficient manner. By taking this course, you'll learn how to use Machine Learning for land use and land cover mapping, which will give you a competitive edge in this field and help you create more informed and sustainable land use plans.

See salaries and explore the career path for Land Use Planner

Natural Resource Manager

Natural Resource Managers work to manage and conserve natural resources such as forests, water, and minerals. This course will provide you with the skills you need to use Machine Learning for land use and land cover mapping, which can be used to assess the condition of natural resources, develop management plans, and monitor the impact of human activities on natural resources.

See salaries and explore the career path for Natural Resource Manager

GIS Specialist

GIS Specialists play a vital role in the analysis and visualization of spatial data for a wide range of applications, including land use and land cover mapping. By taking this course, you'll gain valuable skills in R programming, Machine Learning for image classification, and Remote Sensing, which are all essential tools for GIS Specialists who want to excel in their roles.

See salaries and explore the career path for GIS Specialist

Environmental Scientist

Environmental Scientists use their knowledge of the environment to solve environmental problems. This course will provide you with the skills you need to use Machine Learning for land use and land cover mapping, which can be used to monitor environmental changes, assess the impact of human activities on the environment, and develop strategies to protect and restore the environment.

See salaries and explore the career path for Environmental Scientist

Software Engineer

Software Engineers design, develop, and maintain software applications. This course will provide you with the skills you need to use R for software development, including Machine Learning for image classification. These skills will make you a more valuable asset to any software engineering team.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists combine their knowledge of mathematics, statistics, and programming to extract insights from data. The skills you'll gain from this course, including Machine Learning, R programming, and image classification, will provide you with a solid foundation for a career in Data Science.

See salaries and explore the career path for Data Scientist

Project Manager

Project Managers plan, organize, and execute projects. This course will provide you with the skills you need to use R for project management, including Machine Learning for image classification. These skills will make you a more well-rounded Project Manager.

See salaries and explore the career path for Project Manager

Business Analyst

Business Analysts use data to analyze and improve business processes. This course will provide you with the skills you need to use R for business analysis, including Machine Learning for image classification. These skills will make you a more valuable asset to any business analytics team.

See salaries and explore the career path for Business Analyst