What is Machine Learning?
When one comes across the word “Learn” the first
thought that crosses the mind is the ability
to observe, understand and draw inferences from any news, event, situation or activity
and store it mentally and use it to resolve problems
arising out of similar situations or experiences.
For instance, when learning to drive one is taught the basic functions
and usage of accelerator, clutch and brake and as one starts driving,
one begins to learn when to use which of the 3 either alone or in combination.
So as one comes across situations such as say
traffic jams then one applies the learning
that has been mentally noted in the past to navigate smoothly through a traffic jam.
In due course of time, as one starts to gain expertise, one becomes
adept at applying learning when confronted with situations.
This process of drawing from previous experience and mapping it to present
scenario and deciding the action to be taken happens in fraction of seconds.
This whole process happens inadvertently and is called learning.
When talked about in terms of machine, “Machine Learning” is the process of
developing a computer system in such a way that the machine is trained to observe,
understand and draw inferences of different situations and events and automatically
learn to apply the understanding when confronted with similar experiences.
For this learning, the “machine” which is nothing
but the computer system, is programmed to observe
and store trends and patterns in data, identify
and store the events when the data changed.
This way the machine learns to apply or take decisions when
confronted with situation or event as stored in its memory.
A machine is thus added intelligence by training it to process
large amounts of data and make predictions on that data.
The first step towards making these predictions
is feeding it a large variety of data to process.
When compared to human brain it is similar to exposing the
human brain to varied experiences of different situations
so that a human brain can derive different conclusions
and take decisions when in similar situation.
If the variety of data is limited then a human brain will obviously fail
to take instant decisions when in a never before experienced situation.
The more is the variety of data, the better is the decision making process.
A pre cursor to this is also identifying the variety in the data.
Even subtle differences in the data add to the variety.
However, care also has to be taken to understand that it is not just differences
in data but also similarities that help to categorize them into different groups.
How to categorize the data depends on the problem
or the situation for which decision is to be made.
The whole essence of machine learning is that computers should
be able to take their own decisions by applying stored knowledge
and make predictions on how the data will change or how a
particular event will unfold when given some specific inputs.
In simpler terms, for machine learning, large amount of data set is
fed to a classifier which then segregates data or processes data
based on its internal algorithm or understanding which helps it to
make predictions based on which it can take informed decisions.
A good classification algorithm is one where it is able to
minimize errors by correctly being able to classify the data.
For this a decision boundary has to be correctly defined.
To define the decision boundary the features or
characteristics of data are taken into consideration.
For eg: if asked to categorize a population of runners (people
who practice some form of running on a regular basis)
they can be categorized as those running for marathons,
those sprinting, regular runners and joggers.
Here the decision boundary is the speed, frequency and distance of running.
Also, there may be runners who can fall into
categories of joggers as well as sprinters.
So when asked to correctly determine if a particular runner is a jogger or a sprinter
the speed of running is the feature that would be used as a decision boundary.
Machine Learning and Artificial Intelligence (AI)
are closely related and interchangeably used.
The term “Machine Learning” was first coined by American – Arthur Samuel who
was a pioneer in the field of computer science and artificial intelligence.
It has evolved over the years using different areas
of computational statistics, pattern recognition,
mathematical optimization, computational learning,
predictive analytics and ranking algorithms.
Artificial Intelligence is adding intelligence
to dumb machines making them “smart”.
This means machines would be able to carry out tasks that would make them smart.
Whereas Machine Learning means making a machine independent enough by giving access to
large amount and variety of data such that the machine is able to learn on its own.
So rather than teaching a machine fixed tasks and making it do those tasks, the machine
or the computer system is exposed to huge data and trained to think or learn on its own
by accessing and processing this data.
There are different algorithms that are used to teach
a machine to process data and learn on its own.
Few applications of machine learning in day to day life are GoogleMaps
giving directions to correct an erroneously taken turn while driving,
Facebook giving suggestions on friends that can be added to network,
LinkedIn giving suggestions on the different learning or trainings that a
user can take to hone his/her skills based on past job search history.