If you are a machine learning enthusiast and stay in touch with the latest developments,
you would have definitely come across the news “Machine learning identifies links
between the world's oceans”. Wait, we all know how complex it would be to analyze a
concept such as oceans and their behavior which would undoubtedly involve billions of
data points associated with many critical parameters such as wind velocities, temperatures,
earth’s rotation, etc. Doesn’t this piece of information gives you a glimpse of the
wondrous possibilities of machine learning and its potential uses? And this is just a
drop in the ocean!
So What is ML and Why does it Matter? Machine learning is a segment of artificial
intelligence. It is designed to make computers learn by themselves and perform operations
without human intervention when they are exposed to new data. It means a computer or a system
designed with machine learning will identify, analyze and change accordingly and give the
expected output when it comes across a new pattern of data, without any need of humans.
The power behind machine learning’s self-identification and analysis of new patterns lies in the complex
and powerful ‘pattern recognition’ algorithms that guide them in where to look for what.
Thus, the demand for machine learning programmers who have extensive knowledge of working with
complex mathematical calculations and applying them to big data and AI is growing year after
year.
Machine learning, though a buzz word only since recent times, has conceptually been
in existence since World War II. However, it's only in the past decade or so that there
has been such great progress made in context to machine learning and its uses, driven mainly
by our quest for making this world more futuristic with lesser human intervention and more precision.
Pharma, education technology, industries, science and space, digital inventions, maps
and navigation, robotics you name the domain and you will have instances of machine learning
innovations made in it. So how do machines learn?
Quite simply, machines learn just like humans do. Humans learn from their training, experiences,
and teachers. Sometimes they use the knowledge that is fed into their brains, or sometimes
take decisions by analyzing the current situation using their past experiences.
Similarly, machines learn from the inputs given to them which tell them what is right
and what is wrong. Then they are given data that they would have to analyze based on the
training they have received so far. In some other cases, they do not have any idea of
what is right or wrong, but just take the decision based on their own experiences.
The process of machine learning occurs in five steps.
Gathering the data from varied, rich and dense content of various formats and types which
can include different sources such as text files, word documents or excel sheets.
Preparing the data by extracting the actual data out of the entire content fed. Only the
data that really makes sense to the machine is used for processing. This step also involves
checking for missing data, unwanted data and treatment of outliers.
Training the model using an appropriate algorithm. The data filtered in the second step is split
into two parts. One part is used as training data and the other part is used as reference
data. The training data is used to train the model.
Then the model is evaluated by testing its accuracy. To verify the accuracy to the fullest,
the developed model is tested on such data which was not used during the preparation
stage. Finally, the performance of the machine is
improved by choosing a different model that suits the different type of data that is present
altogether. This is the step where the machine thinks and rethinks in selecting the model
best suited for various types of data.
Voice-based search and speech recognition is one example of machine learning, image
recognition is another. In healthcare, diseases can be diagnosed with machine learning, in
the financial sector, machine learning helps in predicting chances of financial fraud,
customer’s credit habits, spending patterns, etc. These are just a few among many of its
examples. Machine learning models can learn in three
different ways Supervised learning, unsupervised learning, and reinforcement learning.
In supervised learning, as the name suggests, the machine learns under supervision. First,
the machine is trained using predefined data also called ‘labeled’ data. Then, the
correct answer is fed into the computer which allows it to understand what the right and
wrong answers should be. Consider the shape sorting game that kids
play. A bunch of different shapes of wooden pieces are given to kids, say of square shape,
triangular shape, circular shape, and star shape. First, you teach the kids which shape
is what and then you ask them to do the sorting on their own.
Similarly, in machine learning, you teach the machine through labeled data. Then, the
machine is given some unknown data, which it analyses based on the previously labeled
data and gives the correct outcome.
In unsupervised learning, there is no previous knowledge, no previous training, nor a teacher
to supervise. This learning is all instantaneous based on the data that is available at that
given time. Consider a kid playing with a mix of tomatoes
and capsicums. They would sort them involuntarily based on their shape or color. This is an
instantaneous reaction without any predefined set of attributes or training.
A machine working on unsupervised learning would produce the results based on a similar
mechanism. In reinforcement learning, there is no correct
answer known to the system. The system learns from its own experience through a reinforcement
agent. Since the answer is not known, the reinforcement agent decides what to do with
the given task and for this, it uses its experience from the current situation only.
For example, In a robotic game that involves earning the hidden treasure, the algorithm
focuses on bringing out the best outcome through trial and error method. Mainly three components
are observed in this type of learning; the user, the environment and the action the user
is performing. The algorithm adjusts itself accordingly to guide the user towards the
best result that can be achieved. Currently, machine learning is entering our
lives with baby steps. By the next decade, radical changes can be expected in machine
learning and the way it impacts our lives. Customers have already started trusting the
power and comfort of machine learning, and would definitely welcome more such innovations
in the near future. Gartner says
Artificial Intelligence and Machine Learning have reached a critical tipping point and
will increasingly augment and extend virtually every technology-enabled service, thing, or
application. So, it would not be surprising if, in the
future, machine learning would make its entry in almost every aspect of human life, become
omnipresent in business and industries, enter cloud-based services, change the shape of
data, its processing and usage and change the way connected systems work and look owing
to the ever-increasing data on the internet. As a conclusion, we can say, Machine learning
is quite different in its own way. While many experts are raising concerns over the ever-increasing
dependence and the presence of machine learning in our everyday lives, on the positive side,
machine learning can work wonders. And the world is already witnessing its magic – in
health care, finance industry, automotive industry, image processing and voice recognition
and many other fields. While many of us worry that machines may take
over the world, it is totally up to us, how we design effective, yet safe and controllable
machines. There is no doubt that machine learning would change the way we do many things including
education, business, and health services making the world a safer and better place.
So this was an overview of machine learning. Visit our website for different machine learning
courses and don’t forget to subscribe to our channel for more such informative videos.