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The hottest qualification in the Job market even during these testing times are
is Artificial Intelligence, Machine Learning, and Deep Learning, Data Science(not in
any order but they do have some relation). These buzzwords and some more related
to them, being used across industries made me curious to understand what these
technologies are. So, with the help of Google, I am reproducing some facts on the
most sought after qualification in the job market
The buzzwords mentioned above have a common denominator and it is
“DATA. But unlike mathematics, where the higher the denominator lower the result, in
the technologies mentioned above the higher the denominator (i.e DATA) the better
the results.
So What exactly is data Google Defines Data as follows
” facts and statistics collected together or simply any piece of information is data”
So DATA has been existing from time immemorial but what changed the relevance
of DATA is the power of computing and this is what led to the development of Data
Science as a Field and from there further to ML, DL, and AI.
Data Science is the term for a whole set of tools and techniques by which to analyze
data and extract insights from it. It makes use of scientific methods, processes, and
algorithms to make this happen. Essentially, its goal is to discover hidden patterns in
raw data to help businesses improve and increase their profits. Let’s dig deeper.
Every business is set up on the foundation of data, demand survey whether internal
or external data like demand survey, market forecast, customers reviews etc. The
field of data science helps business entrepreneurs to get valuable insight from the
data available and predicts future potential using analytical tools. The process
flow is something like this, The available data is first arranged in a meaningful form (
call it structuring the data), then the data is analyzed for gaining valuable and
meaningful insights, these insights also called Business Intelligence(BI) , used
along with the help of business analytics or data analytics tool can be used to
develop models that can predict future outcomes

Machine Learning(ML in short) is an extension of data science using a computer and
their power to compute volumes of Data. ML is concerned with giving machines the
ability to learn by training algorithms on a huge amount of data. It makes use of
algorithms and statistical models to perform a task without needing explicit
instructions. There are different types of ML, vis-à-vis, Supervised ML, Unsupervised
ML, Reinforced Learning( not elaborate because of the limited scope of this

Deep Learning is an approach to Machine Learning; one that focuses on learning
data representations rather than on task-specific algorithms. It makes use of Deep
Neural Networks, which are inspired by the structure and function of the human
brain. Deep learning is a subset of ML. Just like we use our brains to identify
patterns and classify various types of information, deep learning algorithms can be
taught to accomplish the same tasks for machines.The difference between ML and
DL is very narrow but their use in real time application is enormous. In short DL is a
very fine tuned method of ML but requires enormous amount of data to get there.
Deep Learning improves AI by enabling many of its practical applications- DL makes
it possible.
Artificial Intelligence, (AI in short), is concerned with imparting human-like
intelligence to machines. The aim is to make machines that can think and act like
humans. Such intelligent machines mimic human cognitive functions like learning
and problem-solving.Artificial Intelligence enables the machines to think, means any
equipment weather it’s a computer robot or any other machine which can act on its
own without human intervention.
Examples of AI being Self Driving Cars, Robots, Alexa etc
To put it into perspective, AI was birthed first. Then came in ML, which was an
approach to AI. Finally, DL stormed in and made possible all that was still only to be
dreamed of at the moment. Data and the power of computing made it possible to ties
all these things together

Disclaimer: This content is only a reproduction of Content written by experts
on the Subjects