What Is Machine Learning?
There's no argument about the fact that machine learning is
one of the most powerful and influential technologies in the world. More than
that, even though we feel we've seen everything, we are yet to see it's full
potential. There's no doubt that machine learning will be making and breaking
more headlines over time. Let's learn a little more about machine learning
concepts by covering fundamental ideas.
Machine learning is a tool that turns information into data
and knowledge used in machine learning companies. In the past 50 years or so there has
been a big explosion in data concepts. This mass of data technology is pretty
much useless unless and until we're analysing it and finding what kind of
patterns it hides. Machine learning techniques find hidden valuables that
underline different patterns. Not only that, machine learning techniques
automatically find patterns in complex data that otherwise would be a big
struggle to discover.
What then?
This hidden knowledge and different patterns can be useful in
predicting different kinds of events in the near future and do all kinds of
decision making that is complex. A lot of us are unaware of the fact that we
interact with machine learning all day everyday. Whenever we google a
particular piece of information, listen to music or even take a picture,
machine learning is the engine that runs it all. This way we are constantly
learning and unlearning, improving all the more from every interaction. Not
only that, machine learning is the driving force for self driving cars,
detecting cancer and creating new drugs.
Traditional software engineering combines human rules with
data to give answers to a problem. Machine learning on the other hand makes use
of data to get the rules behind a problem.
Machine learning versus
traditional programming
To learn the rules that govern a particular phenomenon,
machines need to go through a proper learning process trying various rules and learning from the
way they perform. This is why it is known as machine learning. There are
numerous forms of machine learning, supervised, semi supervised, reinforcement
and unsupervised learning. Each form of this learning has a variety of
approaches but they all follow one underlying approach and theory.
Terminology
There is a large set of data examples that contain features
that are important to solve a problem. Important pieces of data that help us
understand a problem are then fed into a machine learning algorithm so that it
can learn. The model then learns from
this data. It is the output you get once you train after an algorithm.
For example, to produce a decision tree model, a decision tree algorithm would
have to be trained.
What is the exact
process?
You need to collect data that the algorithm will learn from.
Then the data preparation bit takes place which extracts important features and
performs dimensionality reduction. This is also known as the fitting stage. This
is the stage where the machine learning
solutions algorithms
actually begins to learn by showing it the data that has been
collected before hand and prepared.
Thank you for the article! Machine learning systems process and analyze enormous amounts of data without explicit programming. They scrutinize previous experience and improve performance on the fly. For example, in our company offshore and nearshore experts have rigid hands-on knowledge of developing self-taught software for Healthcare, Fintech, Aviation, Information and Content Management, Entertainment, and other industries.
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