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Classify space rocks by using Python and artificial intelligence Microsoft Quiz Answers
Get Classify space rocks by using Python and artificial intelligence Microsoft Quiz Answers
This learning path gives you a view into the worlds of AI and space. Learn how to create an AI model that can classify the type of space rock in a random photo.
Tip:
This learning path is part of a multimodal learning experience. Start the first module to see how you can follow along!
Prerequisites:
Module 1: Learn about space rocks and how to classify them
Learn about space rocks, where they’re found, and why they’re valuable for scientific research.
Learning objectives:
In this module, you will:
- Learn what space rocks are and why we study them
- Learn about upcoming space missions that focus on space rocks
Tip:
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
Prerequisites:
None
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What form of space rock might you find on Earth?
- Meteor
- Meteorite
- Asteroid
Q2. What should an astronaut do if they see one black rock in a field of white rocks on a space mission to collect rock specimens?
- Send a photo of the black rock to NASA with a comment about why they think the rock is important.
- Collect the black rock because it’s clearly unique in some way that might be important.
- Collect samples of the white rocks to get an average representation of the area, and also collect the black rock.
Q3. What is the goal of the NASA OSIRIS-REx mission?
- To learn about human history.
- To find an asteroid that’s suitable for humans to inhabit.
- To test a new long-range camera.
Module 2: Prepare to research space rocks by using artificial intelligence
Learn about the scientific research of space rocks and how artificial intelligence can enhance this study.
Learning objectives:
In this module, you will:
- Get an introduction to artificial intelligence
- Discover how humans classify objects
- Discover how machines classify objects
- Learn about artificial intelligence libraries
- Install artificial intelligence libraries
Tip:
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
Prerequisites:
- Ability to write and run basic Python programs
- Visual Studio Code, with the Python extension installed
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What is artificial intelligence (AI)?
- A method that computers might possibly use to take over the world.
- A way to teach a computer how to learn and make basic decisions.
- Primarily a way to automate monotonous tasks that don’t require much thinking.
Q2. What is the role of Anaconda in using Python?
- You can use Anaconda to download one Python library at a time.
- You can use Anaconda to download many Python libraries at once.
- Anaconda allows you to download Python libraries without going online.
Q3. How do humans and machines compare in the way they observe and classify items?
- Like machines, humans have to be trained to see patterns and make associations visually.
- Humans are better than computers at finding minute details in objects
- Like humans, machines use patterns and associations in identified items to classify a new item.
Module 3: Analyze images of rocks by using artificial intelligence
Identify data to add to an artificial intelligence model that classifies space rocks in random photos.
Learning objectives:
In this module, you will:
- Import AI libraries
- Download and import data to use with an AI program
- Learn how to clean and separate data
- Discover how computers read photos as images by using binary format
- Use code to read an image and assign the correct rock type
Tip:
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
Prerequisites:
- Ability to write and run introductory Python programs
- Visual Studio Code, with the Python extension installed
- Artificial intelligence libraries installed
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What is the purpose of separating data into training and testing groups?
- To train the model with one portion of the data and test the model with the rest of the data.
- To limit the amount of data the computer has to process at one time, so it can process more efficiently.
- To train the model more quickly.
Q2. How do computers view images?
- By capturing image captions and adding them to an indexed database.
- By dividing images into four sections and then processing one section at a time.
- By translating the image into ones and zeros.
Q3. How did we clean the data in our AI model?
- By deleting images that were blurry or off-center.
- By resizing all the images, so they were the same size.
- By first using Microsoft Paint to edit some photos.
Module 4: Classify types of space rocks in random photos by using artificial intelligence
Learn how to build an artificial intelligence model to predict types of space rocks in images. Train and test your model by using random photos.
Learning objectives:
In this module, you will:
- Train an artificial intelligence model
- Test the model by using it to classify random photos of space rocks
Tip:
This module is part of a multimodal learning experience. Start the module to see how you can follow along!
Prerequisites:
- Ability to write and run introductory Python programs
- Visual Studio Code, with the Python extension installed
- Artificial intelligence libraries installed
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What is a neural network?
- A computer system that’s modeled on the human brain and nervous system.
- An intricate system of linked computing servers.
- A bundle of software that’s installed on a computer.
Q2. What does accuracy in an AI model refer to?
- The percentage of time a model made an incorrect prediction.
- The percentage of time the model made a correct prediction.
- The number of images you need to train the model for the model to be perfect.
Q3. How can you increase the accuracy of an AI model?
- By training the model five more times.
- By reducing the number of epochs.
- By adding more images and increasing the number of epochs.
Conclusion:
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