>> You're not going to want to miss this episode of AI show,
where Francesca shows us when to use it,
which Machine Learning Algorithm,
and how to do it on the Visual Designer on
the next episode of
the AI Show Special from Microsoft Ignite. See you then.
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>> Hello and welcome to this special edition of
the AI Show coming to you from Microsoft Ignite,
I've got my dear friend and colleague,
Francesca Lazzeri. How are you doing my friend?
>> Very good. Thank you.
>> Fantastic. So look, here's the problem I have.
Anytime I hear about Machine Learning,
I look it up and then there is like 50 bazillion algorithms,
and I don't even know how to start.
So why don't you help us out,
tell us a little bit about Machine Learning Algorithms,
what they are, and then how we'd know how
to select them when we have a problem.
>> Absolutely. Again, this is
the question that customers ask me all the time,
so it's not just you Seth.
>> Okay. Good.
>> Also for these, we wrote
algorithm cheat sheet that you can find on Docs.
So again, everything starts with the questions.
I always tell my customers that Machine Learning,
Deep Learning AI is very fancy,
but if you don't have a business question,
and if you don't have the right data,
then it's very hard to do this.
So you start with a question,
and is although, "What do you want to do?"
For example, let me actually enlarge these.
Okay. For example, you want to
predict between the different categories,
you want to predict between two categories.
So, it depends on what do you do,
there is a different algorithms for you that is reading
in what is called now Azure Machine Learning Designer.
>> I see.
>> This is the module meanings that gives
the specific algorithm that is going to help you to use your data.
So it's going to learn from your data to give you back the answer.
>> That's awesome. So as I'm looking at this,
the first thing I have to do with
anything Machine Learning related,
is I have to be specific about what I'm trying to solve.
Which is like, now that I think about it as a programmer,
it's like you can't just be like, "Hey, write a program."
You have to know what the outcome or objectives are.
>> Absolutely.
>> Here what you're saying is,
depending on what you want to do,
there's a set of algorithms that will help you do that.
>> Yes.
>> So this is awesome. We'll get a link to this in
the show so people can go and look at that.
>> Absolutely.
>> The second thing that you said, that you threw
out kind of fast, and I was like, "Wait a minute,
is there some way to use these insights,
something called Azure Machine Learning,
the visuals." Could you tell us about that?
>> Yes. Azure Machine Learning Designer is
the new version of Azure Machine Learning Studio.
I have to say it's an amazing tool,
and that really is able to unify things.
So we've people of a different technical background,
and as different data scientists are working in teams
where I have to work with that data architect,
business expert, software engineers.
They're using designer really allows
out to see the hand-to-hand process,
so how you can build a Hand-to-Hand Machine Learning solution,
and also have a look at what other people are doing there,
and you can have people who can code using the Designer.
There are some modules for
the Execute a Script, Execute Python Script.
But there are also other modules that are
already created there for you,
and the people who, for example,
don't want to code too much,
they can still build their own Machine Learning solution.
>> Awesome. Can you show us?
>> Of course. So here I have the Designer.
So as you can see, I already created
an experiment for my customers.
This is a Time Series Forecasting for Sales.
So, you just need to input your data,
and here you have a bunch of options in terms of a data set.
These were the data-sets that I linked from a Blob Storage.
Then if you could scroll down,
and you can see that you get the different option in terms of
the data input and output, data transformation,
feature selection, model training, and then,
and the most important part, Machine Learning Algorithm.
For example, if you notice here,
I use a Boosted Decision Tree Regression which is a type of
Regression because whereby Time Series Forecasting Model
was performing very well,
it was able to get a 96 percent accuracy using this data.
Here you can train and score your model,
and the nice things of Designer
is really the deployment experience.
>> Okay.
>> You just need to click on "Run" and then immediately,
it's going to create for you from
training, in predicting experiments,
and the nice things on the predicting experiments is also that
it's going to have two different module,
the Web Service Input and the Web Service Output.
Actually let me show you here.
Let me zoom out.
Here you have the Web Service Input,
and here the Web Service Output,
but the experiment is exactly the same.
Final thing that is exactly as important
as the other things is how are you going to through this model?
Again, Machine Learning AI is beautiful.
But how are you actually going to get the most important value.
So here there is just the Hand Point Intersections,
where you can see from the predictive experiment,
you can get some sample in C# PythonR.
These are just sample code that you can use to consume your model.
Here you can see there are some primary keys,
and they arise at a point.
This is the URI, and you just need to copy these,
insert it using your application,
and your AI application is going to consume
your Machine Learning Model that you build in Designer.
>> This is awesome. Basically, you come up with a question.
You look at the algorithm cheat sheet,
you drag your data and you wire stuff up,
try it out, and see if it works.
It'll tell you if it's good, and then you can
actually release it as an important experiment.
>> Exactly.
>> This is pretty amazing. This is very exciting.
Thank you so much for showing us this.
Thank you so much for watching.
We've learned about what Machine Learning Algorithms to use.
We've also learned how to use a Visual Designer to
actually implement it and use it. Thank you so much again.
>> Thank you.
>> Thank you so much for watching this episode of the AI show
coming to you special from Microsoft Ignite.
We'll see you next time. Take care.
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