Creating a Data Set
Machine Learning - Session 3
Skills I Have Already…
This is the start of your Machine Learning journey. You will be finding out what it is and how it works.
Lesson 1�Introduction to Machine Learning
Lesson 4�Applying the Data Set
Lesson 3�Creating a Data Set
Lesson 2�Trying out Machine Learning
Machine Learning
Skills I Will Learn Next…
Now you know how Machine Learning works, you can think about how you use it and what you need to remember to use it effectively.
Patterns
Prediction
Machine Learning
Label
Features
Algorithm
Training
My iPad Learner Journey
What did we learn last session?
Teaching our Robot
Machine Learning Vocabulary
Testing our Robot
Creating a Data Set
In this session…
This lesson we will be collecting a data set for the CO-ML app that Apple have given us access to. This will include…
How Co-ML Works
We have talked about how machine learning works. We said it’s like teaching a robot by giving it lots of examples, helping it spot patterns, and correcting its mistakes.
We used an example of animals and made cards to make it simpler and easier to understand.
How Co-ML Works
Now, we’re going to use an app called Co-ML, made by Apple, to bring this to life!
This app will help us see how computers can learn to recognise shapes based on what we teach them.
How Co-ML Works
We will teach Co-ML to recognise 4 shapes…
Circle
Triangle
Square
Rectangle
How Co-ML Works
We know from our experience that the more Data we have the better the computer will be at identifying the shape. So we need lots of images of shapes.
Collecting Data
Our Data Set is going to be images of our 4 shapes. We need as many images of circles, squares, triangles and rectangles as possible!
Where can we get the images from?
What do we need to consider when choosing our images? (See next slide)
Collecting Data
Good Images should…
…have the shape in the center of the image.
…be in focus.
…not have other shapes in the image.
…have varied types of shapes.
Collecting Data
In pairs, collect 10 images of each shape.
You can use the internet or take pictures using your iPad camera.
Remember to capture ‘Good Images’ like we discussed earlier.
What might happen if our images aren’t ‘Good’?
Sharing Data
We should now have lots of images of shapes.
If we have all collected the right amount of images we should have over 500 images.
We need to get them all into one place.
To do this we will use AirDrop.
AirDrop
Sharing Data
We will use AirDrop to transfer your Images on to 1 iPad.
We will need to do this carefully, and 1 device/pair at a time.
I will let you know when to do it and help you.
Once we have all of the images on a single device we can start to train our Co-ML model.
AirDrop
Sharing Data
Open your Photos app and select all of your shape images.
AirDrop
Sharing Data
Tap on the ‘Share’ icon in the lower left corner, then AirDrop.
AirDrop
Sharing Data
Now wait for me to come around and transfer your images…
AirDrop
Sharing Data
Now we have all of our images on a single devices we can build and test our model in our next lesson.
Before we finish let’s test ourselves again on our Machine Learning Vocabulary.
Machine Learning Vocabulary - Can you explain them?
Machine Learning
Label
Features
Prediction
Training
Algorithm
Patterns
Data
Machine Learning Vocabulary
Definition: When computers learn from examples instead of being told exactly what to do.
Example: Like teaching a robot to recognize animals by showing it lots of pictures.
Machine Learning
Machine Learning Vocabulary
Definition: Information that computers use to learn.
Example: Pictures, numbers, words, or sounds.
Data
Machine Learning Vocabulary
Definition: Things that happen in the same way or look similar.
Example: Stripes on a zebra or the way birds always have wings.
Patterns
Machine Learning Vocabulary
Definition: A set of instructions that tells a computer how to solve a problem.
Example: Like a recipe that a computer follows to bake a cake.
Algorithm
Machine Learning Vocabulary
Definition: Teaching a computer by giving it lots of examples.
Example: Showing pictures of cats and dogs so it can learn to tell the difference.
Training
Machine Learning Vocabulary
Definition: A guess that the computer makes based on what it has learned.
Example: The computer guesses that a picture is of a cat because it has fur and whiskers.
Prediction
Machine Learning Vocabulary
Definition: The important parts of something that help the computer recognize it.
Example: A bird has feathers, wings, and a beak.
Features
Machine Learning Vocabulary
Definition: A name or category we give to data to help the computer learn.
Example: Labeling a picture of a dog with the word "dog."
Label
Machine Learning Vocabulary - Can you explain them?
Machine Learning
Label
Features
Prediction
Training
Algorithm
Patterns
Data
What have we learnt this session?
Understanding Co-ML
Machine Learning Vocabulary
Sharing our Data
Creating a Data Set
What will we be learning next session?
Next lesson we will be importing our Data Set in to the CO-ML and testing our model out! This will include…
Project Website
This presentation, and all project resources, are available on our project website…