So, now that you've learned all about how to use CreateML to create a machine vision model, it's time 1
to challenge yourself to create your very own machine learning model. 2
Now, in previous modules, we create an app called WhatFlower and we used a research machine learning model 3
that had been trained on maybe 80 or 90 images of each type of flower. 4
Now, as many of you have realized, the machine learning model didn't have enough data to be very accurate. 5
But if you were to use CreateML using all of the intelligence that it has from the Apple Photos and 6
the Apple Siri technologies, and if you were to retrain it using your own images of flowers that you 7
have classified manually, or you have found a data set where it has already been classified, and you were 8
to create your very own flower database machine learning image recognition model, then you can add as 9
much data as you needed to in order for it to become as accurate as you need it to be. 10
Now, there are several other ideas that I've come up with for you to try and create your own machine 11
learning model. 12
Now, depending on what you're interested in, then you can pick and choose or even better come up with 13
your own ideas for some crazy things that you can do. 14
So, for example, you could look for celebrity images on Google Images and try to train a machine learning 15
model that will know the difference between, say, Jennifer Lawrence and Jennifer Aniston, or even better, 16
recognize what celebrity it's being shown. 17
Another fun one is training machine learning model that recognizes brand logos. 18
So you could use the image recognition to figure out which brand is being shown based on their logo. 19
Now, if you have a lot of time and you want to do something really interesting, but quite time consuming, 20
then a really fascinating topic, for me at least, is emotion recognition in people's faces. Now, because 21
the emotions are all displayed on human faces, the differences between, say, surprise and fear, actually, 22
really, really similar. 23
And because the features are not so distinct, then you will need a large data set for each type of emotion 24
based on lots of different types of faces, women, men, people from different countries, and you will need 25
to train your model with a lot more data than some of the simpler things that you could do. 26
Now, with the previous ones I've suggested that involves you going on to, say, Google Image, or wherever 27
else, finding images that you recognize yourself that can be classified. 28
But if you were to try and find public data sets, then some good resources are, for example, 29
kaggle.com. 30
So this is a large machine learning and data science website and I'll include a link in the link resources 31
sheet to this image dataset. 32
So they have a whole bunch of different ones, for example, pictures of the hand that represent different 33
letters in the ASL alphabet, or different flowers, or different monkey species, or CT medical images. 34
And it's really, really fun actually going through these datasets. 35
So Kaggle is one of them. 36
And this GitHub repository also collects a bunch of publicly available open databases of images and 37
classifications. 38
Now, remember that if the evaluation performance that you're getting isn't very good, 39
say, it's below 80 percent, then you might need to retrain your model with more data or make other adjustments. 40
And Apple has created this entire article on improving your models accuracy which I recommend that you 41
read if you are encountering this problem. And they tell you a whole bunch of ways that you can improve 42
the accuracy, for example, by augmenting your images or by getting more data to train your model. 43
And I'll include a link to this in the resources sheet as well. 44
The rule of thumb is that the closer that the images that you're trying to classify resemble each other, 45
even to a human eye, then the more data you need to provide in order for it to pick up on the unique 46
features that differentiate these different classes. 47
Now, a classic example is the blueberry muffin versus chihuahua dog, where I have to say, even my own image 48
classifier in my brain is not often good enough to tell which one's the dog and which one's a muffin. 49
It's very concerning. But I hope you understand that this illustrates the point of the struggles that 50
your machine learning model has to go through to be able to give you an answer with adequate confidence 51
and adequate accuracy. So go crazy, go wild. 52
Create machine learning models that are interesting to you and come up with new ideas. 53
For example, what if you could recognize whether if a person is making a fist or a pair of scissors or 54
paper, and create a rock paper scissors game based off image recognition and your own machine learning 55
models, or what if you could create a game that's based off brand logos. 56
The world is your oyster. 57
And with what you've learned about how to create image recognition models, 58
I hope that you'll use this skill and new knowledge to create and build really fascinating classifiers 59
and models. 60
And once you've done that, and you think it's pretty interesting, and you want to share it with other 61
students on the course, then upload your model to GitHub and show it off in the Q & A section so that 62
other students can be inspired by what it is that you're doing and can add to your database, and can 63
add data or collaborate with you on your projects and your machine learning models. 64
So go crazy, go nuts, have fun, and I look forward to seeing all the fantastic things that you guys achieve 65
in the Q & A sections.