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In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:

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In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:

     Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:

  • Increase specific type of species in certain areas and its relationship with surrounding conditions.

  • Air pollution limits prediction (Good, moderate, unhealthy, Hazardous..)

  • Complex diseases types: potential risk factors and their effects on the disease are investigated to identify risk factors that can be used to develop prevention or intervention strategies.

  • Course application:  Prediction of concentration of particulate matter of less than 10 µm diameter (PM10)

  • This project was published as research articles using similar materials and with major part of analysis (with slight modification to the code). "Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms" in Environmental Science and Pollution Research journal.

    Project 2: Data of Binary labeled target prediction. Target with 2 classes: Yes and No, Slides and No slide, Happened –Not happened, Contaminated- Clean.

  • Flooded areas and it contribution factors like topographic and climate data.

  • Climate change related consequences and its dragging factors like urban heat islands and it relationship with land uses.

  • Oil spills: polluted and non polluted.

  • Course application: Landslide susceptibility mapping in prone area.

  • If you are previously enrolled in my previous course using ANN, then you have the chance to compare the outcomes, as we used the same landslide data here.

Eventually, all the measured data (training and testing), were used to produce the prediction map to be used in further GIS analysis or directly to be presented to decision makers or writing research article in SCI journals.

This course considered the most advanced, in terms of analysis models and output maps that successfully invested in the (1) machine learning algorithm and geospatial domains; (2) free available data of remote sensing in data scarce environment.

IMPORTANT:

LaGriSU Version 2023_03_09 is available (Free) to download using Github link

(search for /Althuwaynee/LaGriSU_Landslide-Grid-and-Slope-Units-QGIS_ToolPack)

*LaGriSU (automatic extraction of training / testing thematic data using Grid and Slope units)

Best regards

Omar AlThuwaynee

Enroll now

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Builds a strong foundation for beginners by assuming little to no background knowledge in supervised classification machine learning, remote sensing, and geospatial resources data
Strengthens existing foundations for intermediate learners by teaching advanced techniques in supervised classification machine learning, remote sensing, and geospatial resources data
Develops professional skills and deep expertise in supervised classification machine learning, remote sensing, and geospatial resources data
Covers unique perspectives and ideas by incorporating real-world applications, such as predicting the concentration of particulate matter and landslide susceptibility mapping
Takes a creative approach to teaching supervised classification machine learning, remote sensing, and geospatial resources data by using the LaGriSU toolpack for automatic extraction of training and testing thematic data
Provides hands-on labs and interactive materials, including access to the free LaGriSU toolpack

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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 Prediction Mapping Using GIS Data and Advanced ML Algorithms with these activities:
Review and Organize Course Materials
Strengthen your understanding by reviewing, organizing, and summarizing course notes, assignments, quizzes, and exams.
Show steps
  • Compile and organize notes from lectures, readings, and other course materials.
  • Create summaries of key concepts and ideas.
Attend Industry Meetups and Conferences on Machine Learning and Geospatial Technologies
Connect with professionals in the field, learn about industry trends, and stay up-to-date on the latest advancements in machine learning and geospatial technologies.
Browse courses on Networking
Show steps
  • Identify relevant industry meetups and conferences.
  • Register for and attend these events.
  • Network with attendees and exchange knowledge and ideas.
Form Study Groups with Classmates
Enhance your understanding by discussing course concepts, sharing knowledge, and working together on assignments with classmates.
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  • Identify classmates with similar interests and learning styles.
  • Schedule regular study sessions to discuss course materials, work on assignments, and prepare for exams.
Four other activities
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Explore Advanced Techniques in Machine Learning for Geospatial Data Analysis
Expand your knowledge by learning about cutting-edge machine learning techniques specifically tailored for geospatial data analysis.
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  • Identify online tutorials and resources covering advanced machine learning techniques for geospatial data analysis.
  • Follow the tutorials to gain practical experience in implementing these techniques.
  • Apply the acquired knowledge to enhance your understanding of the course materials.
Practice Classification Algorithms
Reinforce your understanding of classification algorithms by working through practice problems and exercises.
Browse courses on Classification Algorithms
Show steps
  • Solve practice problems on a variety of classification datasets.
  • Experiment with different classification algorithms to observe their performance on different types of datasets.
  • Analyze the results of your experiments to identify strengths and weaknesses of different algorithms.
Develop a Machine Learning Model for Predicting PM10 Concentration
Gain hands-on experience in developing a machine learning model that can predict PM10 concentration using remote sensing data.
Show steps
  • Gather and preprocess remote sensing and PM10 concentration data.
  • Train and evaluate different machine learning algorithms for PM10 concentration prediction.
  • Select the best performing model and deploy it as a predictive tool.
Contribute to Open Source Projects in Machine Learning for Geospatial Analysis
Gain practical experience and contribute to the community by participating in open source projects related to machine learning for geospatial analysis.
Browse courses on Machine Learning
Show steps
  • Identify open source projects in the field of machine learning for geospatial analysis.
  • Choose a project to contribute to and familiarize yourself with its codebase.
  • Propose and implement improvements or new features to the project.

Career center

Learners who complete Prediction Mapping Using GIS Data and Advanced ML Algorithms will develop knowledge and skills that may be useful to these careers:
Geospatial Analyst
Geospatial analysts analyze geographic data to solve problems and make decisions. They use their knowledge of GIS software and data to create maps, charts, and other visualizations that can help people understand complex issues. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for geospatial analysts. By taking this course, you can gain the skills you need to succeed in this field.
Environmental Scientist
Environmental scientists study the environment and its components, including the air, water, soil, and living organisms. They use their knowledge of science and technology to solve environmental problems and protect the environment. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for environmental scientists. By taking this course, you can gain the skills you need to succeed in this field.
Transportation Planner
Transportation planners develop plans for the development and use of transportation systems. They use their knowledge of GIS data and advanced ML algorithms to create plans that are efficient and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for transportation planners. By taking this course, you can gain the skills you need to succeed in this field.
Water Resources Engineer
Water resources engineers design and manage water resources systems. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for water resources engineers. By taking this course, you can gain the skills you need to succeed in this field.
Urban Planner
Urban planners develop plans for the development and use of land in urban areas. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for urban planners. By taking this course, you can gain the skills you need to succeed in this field.
Planning Director
Planning directors are responsible for developing and implementing plans for the development of a city or town. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for planning directors. By taking this course, you can gain the skills you need to succeed in this field.
Remote Sensing Specialist
Remote sensing specialists use satellite imagery and other data to study the Earth's surface. They use their knowledge of GIS data and advanced ML algorithms to analyze data and create maps that can help people understand complex issues. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for remote sensing specialists. By taking this course, you can gain the skills you need to succeed in this field.
Cartographer
Cartographers create maps and other visual representations of geographic data. They use their knowledge of GIS data and advanced ML algorithms to create maps that are accurate and easy to understand. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for cartographers. By taking this course, you can gain the skills you need to succeed in this field.
Geographic Information Systems (GIS) Manager
GIS managers are responsible for the development and operation of GIS systems. They use their knowledge of GIS data and advanced ML algorithms to create and maintain GIS systems that meet the needs of the organization. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for GIS managers. By taking this course, you can gain the skills you need to succeed in this field.
Data Scientist
Data scientists use their knowledge of data and statistics to solve problems and make decisions. They use their skills in GIS data and advanced ML algorithms to analyze data and create models that can help people understand complex issues. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for data scientists. By taking this course, you can gain the skills you need to succeed in this field.
Conservation Scientist
Conservation scientists develop plans to protect natural resources. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for conservation scientists. By taking this course, you can gain the skills you need to succeed in this field.
Water Resources Specialist
Water resources specialists develop plans to manage water resources. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for water resources specialists. By taking this course, you can gain the skills you need to succeed in this field.
Land Surveyor
Land surveyors measure and map the Earth's surface. They use their knowledge of GIS data and advanced ML algorithms to create maps that are accurate and easy to understand. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for land surveyors. By taking this course, you can gain the skills you need to succeed in this field.
Geographic Information Systems (GIS) Technician
GIS technicians use GIS software to create and maintain GIS systems. They use their knowledge of GIS data and advanced ML algorithms to create maps and other visualizations that can help people understand complex issues. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for GIS technicians. By taking this course, you can gain the skills you need to succeed in this field.
Environmental Planner
Environmental planners develop plans to protect the environment. They use their knowledge of GIS data and advanced ML algorithms to create plans that are sustainable and meet the needs of the community. This course provides a strong foundation in GIS data and advanced ML algorithms, which are essential skills for environmental planners. By taking this course, you can gain the skills you need to succeed in this field.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Prediction Mapping Using GIS Data and Advanced ML Algorithms.
Provides a comprehensive overview of spatial machine learning, covering both supervised and unsupervised learning methods. It useful resource for both beginners and experienced practitioners.
Provides a comprehensive overview of Python for data science and machine learning. It useful resource for both beginners and experienced practitioners.
Provides a comprehensive overview of GIS for environmental applications. It useful resource for both beginners and experienced practitioners.
This comprehensive guide to data mining emphasizes its applications in business intelligence. It covers techniques for data preparation, feature selection, model building, and evaluation. aligns with the course's focus on data analysis and predictive modeling.
This practical guide introduces the concepts and techniques of exploratory data analysis using R. It covers data exploration, visualization, and statistical inference. provides a solid foundation for the course's focus on data analysis and modeling.
This foundational textbook introduces the core concepts and algorithms of data science. It provides a solid mathematical and algorithmic understanding of data analysis. provides background knowledge for the course's focus on machine learning and data analysis.

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