pictures, music, words, spreadsheets, videos, and it

doesn't look like it's going to slow down anytime soon.

Machine learning brings the promise

of deriving meaning from all of that data.

Arthur C. Clarke famously once said,

"Any sufficiently advanced technology is

indistinguishable from magic."

I found machine learning not to be magic,

but rather tools and technology that you

can utilize to answer questions with your data.

This is Cloud AI Adventures.

My name is Yufeng Guo, and each episode,

we will be exploring the art, science,

and tools of machine learning.

Along the way, we'll see just how easy

it is to create amazing experiences

and yield valuable insights.

The value of machine learning is only

just beginning to show itself.

There is a lot of data in the world today generated

not only by people, but also by computers, phones

and other devices.

This will only continue to grow in the years to come.

Traditionally, humans have analyzed data

and adapted systems to the changes in data patterns.

However, as the volume of data surpasses

the ability for humans to make sense of it

and manually write those rules, we

will turn increasingly to automated systems that

can learn from the data and importantly,

the changes in data to adapt to a shifting landscape.

We see machine learning all around us

in the products we use today.

However, it isn't always apparent

that machine learning is behind it all.

While things like tagging objects and people inside

of photos are clearly machine learning at play,

it may not be immediately apparent

that recommending the next video to watch

is also powered by machine learning.

Of course, perhaps the biggest example of all

is Google search.

Every time you use Google search,

you're using a system that has many machine learning systems

at its core, from understanding the text of your query

to adjusting the results based on your personal interests,

such as knowing which results to show you first when searching

for Java depending on whether you're a coffee expert

or a developer-- perhaps you're both.

Today, machine learning's immediate applications

are already quite wide-ranging, including image recognition,

fraud detection and recommendation systems,

as well as text and speech systems too.

These powerful capabilities can be

applied to a wide range of fields,

from diabetic retinopathy and skin cancer detection to retail

and of course, transportation in the form

of self-parking and self-driving vehicles.

It wasn't that long ago that when a company or product had

machine learning in its offerings,

it was considered novel.

Now, every company is pivoting to use machine learning

in their products in some way.

It's rapidly becoming, well, an expected feature.

Just as we expect companies to have a website that

works on your mobile device or perhaps an app,

the day will soon come when it will

be expected that our technology will

be personalized, insightful and self-correcting.

As we use machine learning to make human tasks better, faster

and easier than before, we can also

look further into the future when machine learning

can help us do tasks that we never

could have achieved on our own.

Thankfully, it's not hard to take advantage

of machine learning today.

The tooling has gotten quite good.

All you need is data, developers and a willingness

to take the plunge.

For our purposes, I've shortened the definition

of machine learning down to just five words--

using data to answer questions.

While I wouldn't use such a short answer

for an essay prompt on exam, it serves a useful purpose for us

here.

In particular, we can split the definition into two parts--

using data and answer questions.

These two pieces broadly outline the two sides

in machine learning, both of them equally important.

Using data is what we refer to as training,

while answering questions is referred to as making

predictions or inference.

Now let's drill into those two sides briefly for a little bit.

Training refers to using our data

to inform the creation and fine tuning of a predictive model.

This predictive model can then be

used to serve up predictions on previously unseen data

and answer those questions.

As more data is gathered, the model

can be improved over time and new predictive models deployed.

As you may have noticed, the key component

of this entire process is data.

Everything hinges on data.

Data is the key to unlocking machine learning, just

as much as machine learning is the key to unlocking

that hidden insight in data.

This was just a high level overview

of machine learning-- why it's useful

and some of its applications.

Machine learning is a broad field,

spanning an entire family of techniques when

inferring answers from data.

So in future episodes, we'll aim to give you

a better sense of what approaches

to use for a given data set and question

you want to answer, as well as provide the tools for how

to accomplish it.

In our next episode, we'll dive right

into the concrete process of doing machine learning

in more detail, going through a step-by-step formula for how

to approach machine learning problems.

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