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<h2>10 Intresting Facts about Machine Learning</h2>
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<p style="color:#333">
Machine learning is about creating algorithms and systems that can
learn from the data they process and analyze. The more data is
processed, the better the algorithm will become. It is actually a
science of getting computers to act without explicitly being
programmed and is a branch of Artificial Intelligence (AI). But
right now before the possible alarming impact of artificial
intelligence, we could in the today, the now, enjoy learning about
some interesting facts.
<br />
<br />
<strong
>In this article, you will know 10 such interesting facts about
machine learning!</strong
>
<br />
<br />
<strong style="color: darkblue"
>1: Facebook’s News Feed uses machine learning to personalize
each member’s feed.</strong
>
Facebook’s News Feed uses machine learning to personalize each
member’s feed. If a member frequently stops scrolling in order to
read or “like” a particular friend’s posts, the News Feed will
start to show more of that friend’s activity earlier in the feed.
Behind the scenes, the software is simply using statistical
analysis and predictive analytics to identify patterns in the
user’s data and use to patterns to populate the News Feed. Should
the member no longer stop to read, like or comment on the friend’s
posts, that new data will be included in the dataset and the News
Feed will adjust accordingly.
<br />
<br />
<strong style="color: darkblue"
>2: Machine learning ignores preexisting knowledge.</strong
>
Real knowledge is the result of a long process of reasoning and
experimentation, which you can’t mimic by running a generic
algorithm on a database. Experts in different fields have invested
the significant human effort to develop domain knowledge. Domain
experts are often better than machines at suggesting features that
hold predictive power. As such, domain experts form a key part in
defining the input to a machine learning system, from which
preexisting knowledge can be extracted, extended, and refined.
<br />
<br />
<strong style="color: darkblue"
>3: speech recognition by Apple’s Siri or Facebook’s
controversial facial recognition technology are great examples
of the power of machine learning.</strong
>
Genuine learning is the after effect of a long procedure of
thinking and experimentation, which you can’t emulate by running a
nonexclusive calculation on a database. And, this can be seen
quite well when you will get your focus into Apple’s Siri or
Facebook’s controversial facial recognition technology which uses
an extraordinary algorithm to work and rely on the user’s tasks
and working. 4: Banks and other businesses in the financial
industry use machine learning technology for two key purposes: to
identify important insights in data, and prevent fraud. The
insights can identify investment opportunities, or help investors
know when to trade. Data mining which is a part of machine
learning can also identify clients with high-risk profiles, or use
cyber surveillance to pinpoint warning signs of fraud. 5: Machine
learning is applied in weather forecasting software to improve the
quality of the forecast. Deep learning, a branch of machine
learning, is one technique that is showing promise in the field of
weather prediction. Deep learning allows researchers to process,
analyze and enact on extremely large data sets by leveraging a
series of trained algorithms that can learn and make predictions
based on past data of weather reports.
<br />
<br />
<strong style="color: darkblue"
>6: Machine learning, reorganized as a separate field, started
to flourish in the 1990s.</strong
>
Machine learning, reorganized as a separate field, started to
flourish in the 1990s. The field changed its goal from achieving
artificial intelligence to tackling solvable problems of a
practical nature. It shifted focus away from the symbolic
approaches it had inherited from AI, and toward methods and models
borrowed from statistics and probability theory. It also benefited
from the increasing availability of digitized information, and the
ability to distribute it via the Internet.
<br />
<br />
<strong style="color: darkblue"
>7: Machine Learning can only discover correlations, not causal
relationships.</strong
>
In fact, one of the most popular types of machine learning
consists of trying out different actions and observing their
consequences: the essence of causal discovery. For example, an
e-commerce site can try many different ways of presenting a
product and choose the one that leads to the most purchases.
You’ve probably participated in thousands of these experiments
without knowing it. And causal relationships can be discovered
even in some situations where experiments are out of the question,
and all the computer can do is look at past data.
<br />
<br />
<strong style="color: darkblue"
>8: The ”dumb” approach of machine learning eventually beats
human experts.</strong
>
Research in many fields (like linguistics/translation) over the
last 40 years has shown that these generic learning algorithms
that “stir the number stew” out-perform approaches where real
people try to come up with explicit rules themselves. The “dumb”
approach of machine learning eventually beats human experts.
<br />
<br />
<strong style="color: darkblue"
>9: ML Saves Lives! Computer-Aided Detection (CAD) software can
spot 52% of breast cancer cells, a year before patients are
diagnosed.</strong
>
Computer-aided detection (CADe) and diagnosis (CAD) has been a
rapidly growing, active area of research in medical imaging.
Machine learning (ML) plays an essential role in CAD, because
objects such as lesions and organs may not be represented
accurately by a simple equation; thus, medical pattern recognition
essentially requires “learning from examples.” One of the most
popular uses of ML is the classification of objects such as lesion
candidates into certain classes (e.g., abnormal or normal, and
lesions or non-lesions) based on input features (e.g., contrast
and area) obtained from segmented lesion candidates.
<br />
<br />
<strong style="color: darkblue"
>10: Machine learning combined with linguistic rule creation
helps to know what customers are saying about you on
twitter.</strong
>
Machine learning can also be combined with linguistic rules
creation. This application is implemented by Twitter, where you
will know what customers say about you.
</p>
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<h1>Recommended</h1>
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<h3>Tasks Performed by an Operating System</h3>
<p>
More recently, operating systems have started to pop up in
smaller computers as well. If you like to tinker with electronic
devices, you're probably pleased that operating systems can now
be found on many of the devices we use every day, from cell
phones to wireless access points.
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<h3 style="color:#fff">How to crack A technical Interview</h3>
<p>
Getting a job after graduation or during bachelor’s Degree is
sometimes proves to be a nightmare for a fresher. Most of the
students, most of the time clears all the rounds but fails to
clear the final round ie; The Technical Interview, which becomes
a hurdle for them, In this article we will Meet the final hurdle
between you and getting a job. The technical interview.
</p>
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<h3>Getting started in Datascience</h3>
<p>
There's no denying that 'data scientist' is a hot job title to
have right now, and for good reason. It's a tremendously fun and
challenging field to be in, and despite all of the often
underserved hoopla that surrounds it, data scientists are doing
some pretty amazing things. So it's no surprise that many people
are clamoring to find out how to become data scientists.
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