>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<< What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
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How Google does Machine Learning
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Course 1 of 5 in the Specialization Machine Learning with TensorFlow on Google Cloud Platform
From the lesson
Python notebooks in the cloud
This module covers Cloud Datalab, which is the development environment you will use in this specialization.
Meet the Instructors
Google Cloud Training
So in this lab, we're going to look at how to invoke
machine learning APIs from within Datalab.
So let's go ahead.
And this time, instead of going ahead and doing this Notebook from scratch,
we will start from a Notebook that's already in our GitHub repository.
So first, we need to check it out.
So let's go ahead and check out the Notebook.
And in order to do that you have to clone the repo.
So, we'll go ahead and open up a Datalab,
and then run a Bash command from within Datalab.
So, the idea here is that we can go ahead and start a new Notebook.
Call this Notebook whatever we want.
Let's call it, Checkout.
Now, so far we've basically looked at running Python code within Datalab but by putting
in percent Bash this makes Datalab run everything in that cell using Bash.
So, this is like everything else in Jupyter.
So here I'm basically going ahead and doing a Git clone of our repo
and let's go ahead and do that.
So at this point,
I can do bangle S,
that's another way to run Bash.
And you will notice that there is a folder called training data analyst.
And, we can now go ahead and load up that Notebook and start executing it.
So there is training data analyst.
And this time, what you want to do is to go into courses, machine learning,
deepdive, and open up the ML APIs that IPython Notebook,
and there is our Notebook.
So, the first thing to do is to go ahead and enable APIs and Services.
And so that we can run the Vision API,
and the Translate API,
and the Speech API, et cetera.
So, we go down here
and type in vision.
And, there is the Vision API
and the API is enabled.
Let's go ahead and do the same thing for Translate and Speech.
There's the Google Translation API that's also enabled already.
And the Natural language API,
there it is, that's enabled as well.
And, the Speech API, let's just make sure it's also enabled.
And that's also enabled.
So great. So all of the APIS are enabled.
So, let's go ahead and get the Credentials.
So, we'll go down to the APIs and Services,
and get the Credentials.
So, we already have the API key.
So, I went ahead and used it.
Or we can go ahead and say,
create Credentials with an API key,
and create a brand new key,
copy that and there we go.
So, that's our API key.
So, here it is.
And now, we're ready to go into the ML APIs.
And in our Notebook where it says API key,
I will replace by the new API key that we have, and run it.
So, I can either click the run button,
or I can do shift enter.
So, let's go ahead and
install the Python client.
So having done that,
let's go ahead, and run the Translate API.
And you notice that there is the inputs,
is it really this easy?
And you see the translation in French because we asked for the target to be French.
Let's change the target to be ES,
that's Espaniol, and run it.
And now, what we get back is Spanish.
So, how does this work?
We went ahead and specified the inputs as an array of strings,
and asked the service to go ahead and do a translation from English
to whichever language we want passing in those inputs.
And what we got back is the outputs, the translated string.
Similarly, what you want to do is to go ahead and invoke the Vision API.
And to invoke the Vision API,
we need an image.
And in this case, the image is the image of a street sign.
I don't know Chinese so I don't know exactly what it says.
Let's see what it says. So we'll go ahead and put this on Cloud storage.
So this is actually been made public so we don't have to change anything here.
We can go ahead and read,
we can ask the Vision API to read that image,
and tell us what text is in it.
So we can go ahead and run that.
And at this point, we get back the JSON output.
So again, what we're doing here is that we're invoking the version one of the Vision API,
passing in the GCS image URI.
GCS meaning, again, Google Cloud Storage.
We have this image on cloud storage.
We could also pass an image as part of
our request but having it on cloud storage makes it faster.
Because we don't have to upload all of that image data along with our request.
And we are asking it to do text detection,
and what comes back is all of the text in this image,
along with the language ZH meaning Chinese,
and a bounding polygon of each of those pieces of text.
We could of course go ahead and get the first piece of it,
and take the text annotation,
get the language, the locale which we said was ZH.
And then, we could go ahead and print out what we got,
and we got back the foreign language to ZH,
and the foreign text which is all of this.
So now, what we can do is to go ahead and run it.
Of course, the result of it having been drawn is already
here so I can click on this cell, clear it.
And then now, you can run it again,
and you can make sure that what you are being run is yours,
and we see that the Chinese text has now been translated into English.
The other thing that we can do is the Language API.
So here, we have a set of quotes.
And what we want to do is to look at the sentiment associated with these quotes.
So again as before,
let's go ahead and clear the cell and run it.
So in this case,
we are printing out the polarity and the magnitude,
all associated with each of these codes.
So, the polarity is positive,
if it's a positive sentiment,
it's negative if it's a negative sentiment.
And that makes sense.
If you say, to succeed you must have tremendous perseverance,
that's a very positive thing.
But, if you say for example,
when someone you love dies.
Well, that's a pretty negative thing.
So polarity is negative.
And the magnitude is an indicator of how
often very strongly worded language occurs in the text.
The final piece that we're showing up here is a Speech API.
And as before, we have an audio file loaded into cloud storage and we
are asking for the result of that speech to be made into text.
So, we can go ahead and run that,
and we get back a JSON response.
And the JSON responds at a very high confidence is that the speech in that audio file is,
"How old is the Brooklyn Bridge?"
So what we have done in this lab is that we've used
Datalab to use Python APIs to essentially invoke the machine learning models.
So remember that these are not machine learning models that we had to build.
These are machine learning models that we could just go ahead and use.
We could incorporate this machine learning models into our own applications.
This is something that you want to recognize that
not every ML thing that you need to do has to be done from scratch.
If what you want to do is to recognize text and images,
you might just use the Vision API.
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