The world is filled with data, a lot of
data--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. 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 adapt 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 the 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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