Today,
companies are using Machine Learning to improve business decisions, increase
productivity, detect disease, forecast weather, and do many more things. With
the exponential growth of technology, we not only need better tools
to understand the data we currently have, but we also need to prepare
ourselves for the data we will have. To achieve this goal we need to build
intelligent machines. We can write a program to do simple things. But for
most of times Hardwiring Intelligence in it is difficult. Best way to do
it is to have some way for machines to learn things themselves. A
mechanism for learning, if a machine can learn from input then it does the
hard work for us. This is where Machine Learning comes in action.
Some examples
of machine learning are:
§ Database Mining for growth of automation: Typical applications include Web-click data for better UX (User experience), Medical records
for better automation in healthcare, biological data and many more.
§ Applications that cannot be programmed: There are some tasks that
cannot be programmed as the computers we use are not modelled that way.
Examples include Autonomous Driving, Recognition tasks from unordered data
(Face Recognition/ Handwriting Recognition), Natural language Processing, computer Vision etc.
§ Understanding Human Learning: This is the closest we have understood
and mimicked the human brain. It is the start of a new revolution, the real
AI. Now, after a brief insight lets come to a more formal definition of
Machine Learning
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