Machine learning & the need for it à
Machine
learning is a sub field of Artificial Intelligence, in which a computer
system is fed with algorithms that are designed to analyze &
interpret different types of data on their own. These learning
algorithms obtain the analyzing ability when they are trained for the
same using sample data.
It comes in handy when the amount of data
to be analyzed is very large & out of human limits. It can be used
to arrive at important conclusions & make important decisions.
Some important fields where it is being implemented:

Cancer treatment
Chemotherapy, which is used in killing cancerous cells poses the danger
of killing even the healthy cells in the human body. An effective
alternative to chemotherapy is radiotherapy which makes use of machine
learning algorithms to make the right distinction between cells.

Robotic surgery
Using this technology, risk free operations can be performed in parts of
the human body where the spaces are narrow & the risk of a doctor
messing up the surgery is high. Robotic surgery is trained using machine
learning algorithms.

Finance
It is used to detect fraudulent bank transactions within seconds for which a human would take hours to realize.
The utility of Machine learning is endless & can be used in multiple fields.
What does one learn in Machine Learning?

Supervised algorithms
Supervised learning is the type of learning in which input & output
is known, & you write an algorithm to learn the mapping process or
relation between them.
Most algorithms are based on supervised learning.

Unsupervised algorithms
In unsupervised learning, the output is unknown & the algorithms
must be written in a way that makes them selfsufficient in determining
the structure & distribution of data.
Prerequisites
Computer
science students & other students with an engineering background
find it easier to learn Machine learning. However, anybody with good or
at least a basic knowledge in the following domains can master the
subject at beginner level: –

Fundamentals of programming
Fundamentals of programming include a good grip of basic programming, data structures & its algorithms.

Probability & statistics
Key probability topics like axioms & rules, Baye’s theorem, regression etc. must be known.
Knowledge
on statistical topics like mean, median, mode, variance, &
distributions like normal, Poisson, binomial etc. is required.

Linear Algebra
Linear algebra is the representation of linear expressions in the form
of matrices & vector spaces. For this, one must be well informed
about topics like matrices, complex numbers & polynomial equations.
NOTE: These prerequisites are for beginners.
Job prospects in Machine learning à
Owing
to its limitless applications & use in modern & improvised
technology, demand for its professionals is increasing day by day, &
it would never ever go out of trend.
A professional can find jobs in the following fields: –

Machine learning engineer

Data engineer

Data analyst

Data scientist
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