In this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.
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In this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.
In this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.
Note: Who having IDRISI Taiga They can also follow same steps.
This course covers 90% Practical and 10% Theory.
Don’t hesitate to ask me Questions in QA Session.
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Activities
Reading about GIS and Machine Learning for Landuse Prediction
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Students can refresh their knowledge on this topic before the course begins.
View
Machine Learning in Action
on Amazon
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-
Read the first three chapters of the book
-
Summarize the main points of each chapter
-
Write a one-page review of the book
Watch the video tutorials on the course topics
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Watching the video tutorials can help students learn the course material at their own pace.
Browse courses on
Machine Learning
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-
Watch the video tutorials on the course topics
-
Take notes on the key points
-
Review the videos if you need to
Create a concept map of the course material
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Creating a concept map can help students visualize the relationships between different concepts and theories.
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Machine Learning
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-
Identify the main concepts covered in the course
-
Draw a diagram that shows the relationships between the concepts
-
Write a brief explanation of each concept and its relationship to the other concepts
Five other activities
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Show all eight activities
Attend a networking event for professionals in the field
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Attending a networking event can help students connect with other professionals in the field and learn about potential job opportunities.
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Machine Learning
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-
Register for the event
-
Attend the event
-
Find a networking event for professionals in the field
-
Introduce yourself to other attendees
-
Exchange contact information with other attendees
Complete the practice exercises in the course
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Completing the practice exercises can help students reinforce their understanding of the course material.
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Machine Learning
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-
Complete the practice exercises at the end of each chapter
-
Check your answers against the answer key
-
Review the concepts that you struggled with
Attend a workshop on machine learning or landuse change modeling
Show steps
Attending a workshop can help students learn more about the course material and network with other professionals.
Browse courses on
Machine Learning
Show steps
-
Find a workshop on machine learning or landuse change modeling
-
Register for the workshop
-
Attend the workshop
-
Take notes on the key points
-
Follow up with the workshop organizers or speakers if you have any questions
Create a presentation on the course material
Show steps
Creating a presentation can help students synthesize their understanding of the course material.
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Machine Learning
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-
Choose a topic for your presentation
-
Research your topic
-
Create a PowerPoint presentation
-
Practice your presentation
-
Deliver your presentation to the class
Start a project that uses machine learning or landuse change modeling
Show steps
Starting a project can help students apply their knowledge of the course material and gain hands-on experience.
Browse courses on
Machine Learning
Show steps
-
Choose a project idea
-
Develop a project plan
-
Gather data
-
Build a model
-
Evaluate your model
Reading about GIS and Machine Learning for Landuse Prediction
Show steps
Students can refresh their knowledge on this topic before the course begins.
View
Machine Learning in Action
on Amazon
Show steps
- Read the first three chapters of the book
- Summarize the main points of each chapter
- Write a one-page review of the book
Watch the video tutorials on the course topics
Show steps
Watching the video tutorials can help students learn the course material at their own pace.
Browse courses on
Machine Learning
Show steps
- Watch the video tutorials on the course topics
- Take notes on the key points
- Review the videos if you need to
Create a concept map of the course material
Show steps
Creating a concept map can help students visualize the relationships between different concepts and theories.
Browse courses on
Machine Learning
Show steps
- Identify the main concepts covered in the course
- Draw a diagram that shows the relationships between the concepts
- Write a brief explanation of each concept and its relationship to the other concepts
Attend a networking event for professionals in the field
Show steps
Attending a networking event can help students connect with other professionals in the field and learn about potential job opportunities.
Browse courses on
Machine Learning
Show steps
- Register for the event
- Attend the event
- Find a networking event for professionals in the field
- Introduce yourself to other attendees
- Exchange contact information with other attendees
Complete the practice exercises in the course
Show steps
Completing the practice exercises can help students reinforce their understanding of the course material.
Browse courses on
Machine Learning
Show steps
- Complete the practice exercises at the end of each chapter
- Check your answers against the answer key
- Review the concepts that you struggled with
Attend a workshop on machine learning or landuse change modeling
Show steps
Attending a workshop can help students learn more about the course material and network with other professionals.
Browse courses on
Machine Learning
Show steps
- Find a workshop on machine learning or landuse change modeling
- Register for the workshop
- Attend the workshop
- Take notes on the key points
- Follow up with the workshop organizers or speakers if you have any questions
Create a presentation on the course material
Show steps
Creating a presentation can help students synthesize their understanding of the course material.
Browse courses on
Machine Learning
Show steps
- Choose a topic for your presentation
- Research your topic
- Create a PowerPoint presentation
- Practice your presentation
- Deliver your presentation to the class
Start a project that uses machine learning or landuse change modeling
Show steps
Starting a project can help students apply their knowledge of the course material and gain hands-on experience.
Browse courses on
Machine Learning
Show steps
- Choose a project idea
- Develop a project plan
- Gather data
- Build a model
- Evaluate your model
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