Quick Guide To Machine Learning: What It Is And How It Works? - PowerPoint PPT Presentation

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Quick Guide To Machine Learning: What It Is And How It Works?

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Machine learning tailors your feed to your precise interests and behavioral patterns. If you use a smartphone, machine learning is what lets you search for keywords in your photo app, allowing the device to know which pictures are of “trees” and which ones are of “cats.” – PowerPoint PPT presentation

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Title: Quick Guide To Machine Learning: What It Is And How It Works?


1
Quick Guide To Machine Learning What It Is And
How It Works?
Email ID- info_at_clarifai.com
2
About Clarifai
  • Clarifai is accelerating the progress of
    humanity with continually improving AI.
  • Founded in 2013 by Matthew Zeiler, a foremost
    expert in machine learning, Clarifai has been a
    market leader since winning the top five places
    in image classification at the ImageNet 2013
    competition.
  • Recognized by leading industry analysts for our
    award-winning platform, Clarifai offers an
    end-to-end solution for modeling unstructured
    data for the entire AI lifecycle. Our powerful
    image, video, and text recognition solutions are
    built on the most advanced machine learning
    platform and made easily accessible via API,
    device SDK, and on-premise, empowering businesses
    all over the world to build a new generation of
    intelligent applications.

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3
Machine learning is all around you
Youre likely reading this article because some
algorithm brought you to it. Maybe it was through
a search engine like Google, who now uses ML to
tailor search results. Or think about a social
platform like Facebook, machine learning tailors
your feed to your precise interests and
behavioral patterns. If you use a smartphone,
machine learning is what lets you search for
keywords in your photo app, allowing the device
to know which pictures are of trees and which
ones are of cats. Googles photo app even
proactively combines similar photos in time and
subject matter to periodically offer pre-made
movies for you.When you buy something online,
machine learning likely leads you to find that
specific item, and then your credit card company
uses machine learning to decide if the
transaction is fraudulent. The stock market is
chock full of ML algorithms trading with other ML
algorithms. Now, customer service uses helpful
little chatbots willing to automate every journey
of your customer support experience. Since the
world is full of these machines that have somehow
studied their way into usefulness, lets see how
they work.
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Machine Learning vs. Programming
Computer programs have traditionally been built
upon rules-based logic that provide computers
with very specific instructions about what they
should be doing. This rules-based approach
usually contains a complex system of conditional
statements, like if this, then that. For
example, a video game might contain instructions
to shoot a laser when a given button is pressed.
Even the device youre using to read this blog
article is full of rules-based logic.But there
are limitations to this rules-based approach. For
one, its not very intelligent. With rules-based
programming, you have to provide explicit
instructions for absolutely everything you want
your computer to do. It turns out that this is a
very painstaking process, and for complex
problems like interpreting images or
understanding natural human language, the
rules-based approach to programming has made
relatively little progress over the years.The
fact is that many problems are too difficult to
program using regular programming. Enter machine
learning. It focuses on the use of data and
algorithms to imitate the way that humans learn,
gradually improving its accuracy, just as a human
does when learning something new. With ML,
computers are given huge amounts of training
data which they use to learn how to perform a
given task.
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How Does Machine Learning Work?
The beginning phases of Machine Learning saw
tests including speculations of PCs perceiving
designs in information and gaining from them.
Today, in the wake of expanding upon those basic
tests, AI is more mind boggling. While Machine
learning calculations have been around for quite
a while, the capacity to apply complex
calculations to enormous information applications
all the more quickly and viably is a later turn
of events. Having the option to do these things
with some level of refinement can set an
organization in front of its rivals. ML is a type
of man-made brainpower (AI) that helps PCs to
think along these lines to how people do
Learning and developing past encounters. It works
by investigating information and recognizing
designs and includes insignificant human
mediation. Practically any errand that can be
finished with an information characterized
example or set of rules can be robotized with AI.
This permits organizations to change measures
that were beforehand an option exclusively for
people to performthink reacting to client
support calls, accounting, and investigating
resumes. 
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The three main types of machine learning models
Supervised learning This is the type of machine
learning that is most similar to the way humans
learn. You give the computer a bunch of labelled
training data, and the data acts as the teacher
telling it what to learn. For example, you can
give it 1,000 photos of houses and tell it these
are houses, and another 1,000 photos without
houses, and tell it these are not houses,
machine learning algorithms can identify patterns
in data. As similar photos are fed into the
algorithm, it will begin to understand whether a
house is present it or not. In other words,
historic data contains correct answers, and the
task of the algorithm is to find them in the new
data. Common use cases for supervised learning
are predicting future trends in prices, sales and
stock trading based on past data.
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Unsupervised learning This type of machine
learning looks at a big set of unlabelled and
unclassified data and makes inferences on it
based on its structure. Basically, the machine is
left on its own to find patterns in the data. It
groups unsorted information according to
similarities and differences even though there
are no categories provided. Its commonly used in
the fields of digital marketing and advertising
and is essentially useful in recommender systems.
Amazon and Netflix make very effective use of
machine learning to power product recommender
systems. They use ML to look at data to find
patterns of similarities of peoples preferences
for products and media consumption. Youve likely
seen this as People who viewed this product,
also viewed this one.
Reinforcement learning In reinforcement
learning, computers are trained on a reward and
punishment mechanism. RL is a complex and
challenging method, but can deliver impressive
results for some use cases by learning via
interaction and feedback trial and error. The
machine is rewarded for correct moves and
punished for the wrong ones. In doing so, the
agent tries to minimize wrong moves and maximize
the right ones.The machine can begin to perceive
and interpret its environment and take actions
and interact with it. One remarkable application
of reinforcement learning is AlphaGo Zero, a
model that drives the game Go. Using
reinforcement learning, AlphaGo Zero was able to
learn the game of Go from scratch. It learned by
playing against itself, and after 40 days of
self-training, Alpha Go Zero was able to
outperform a previous iteration of Alpha Go one
which had defeated the world champion.
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The Future Machine learning is expected to grow
in the U.S. alone from what was 1.03 billion USD
in 2016 to 8.81 billion USD by 2022. Machine
learning driven solutions are present in
countless customer experiences, and their
competitive edge is increasing as big players
like Google, IBM and Microsoft continue to make
advancements in this field.
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Conclusion Machine learning adoption is growing
steadily. Be it video-detection systems in road
transportation, self-driving cars, 3D printing in
manufacturing, facial recognition, healthcare,
and advanced sensors in defense and logistics,
computer vision technology is being used
extensively. In the present world, machine
learning can bring immense financial gains in
business. However, this is just the tip of the
iceberg. This technology has immense potential in
the future.  Check out Clarifai, the leading deep
learning platform for computer vision, natural
language processing, and automatic speech
recognition.
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https//www.facebook.com/Clarifai
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