Machine learning is a subset of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It involves the use of algorithms to recognize patterns in data in order to make predictions or decisions without being explicitly programmed to perform the specific tasks. There are two main types of machine learning: supervised learning which uses labeled data to predict outputs, and unsupervised learning which finds hidden patterns in unlabeled data. Machine learning has many applications and enables organizations to analyze complex data automatically to make data-driven decisions.

1. -C T PAWAN
SCHOOL OF ECE
REVA UNIVERSITY

2. Machine learning is a subset of computer science and a branch of artificial intelligence. It
focuses primarily on the study and construction of algorithms that can learn and make
predictions based on data, as well as overcome program limitations and make data-
driven decisions.
The term ‘machine learning’ was initially coined by Arthur Samuel in 1959 and is defined
as a “computer’s ability to learn without being explicitly programmed”. Machine
learning has grown in popularity since then – and we have arrived at the point where it
is profitable and possible for businesses to utilise machine learning to improve efficiency.
Machine learning was born from pattern recognition and the notion that computers can
learn without being programmed to perform specific tasks. In helping machines to learn
without being programmed to do so, the machine can adapt and improve over time.
By using algorithms, machines can learn – and different algorithms learn in different
ways. As machine learning algorithms are exposed to new data sets they adapt over time
and increase their ‘intelligence’.
WHAT IS MACHINE LEARNING?

3. WHY MACHINE LEARNING?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability
to automatically learn and improve from experience without being explicitly
programmed. Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or
instruction, in order to look for patterns in data and make better decisions in the future based on
the examples that we provide. The primary aim is to allow the computers learn
automatically without human intervention or assistance and adjust actions accordingly.

4. Machine learning uses two types of techniques: supervised learning, which
trains a model on known input and output data so that it can predict future
outputs, and unsupervised learning, which finds hidden patterns or intrinsic
structures in input data.
HOW MACHINE LEARNING WORKS?
Supervised Learning: It builds a model that makes predictions based on
evidence in the presence of uncertainty. A supervised learning algorithm takes
a known set of input data and known responses to the data (output) and trains a
model to generate reasonable predictions for the response to new data. Use
supervised learning if you have known data for the output you are trying to
predict. Supervised learning uses classification and regression techniques to
develop predictive models.

5. Unsupervised learning :It finds hidden patterns or intrinsic structures in data. It is used to
draw inferences from datasets consisting of input data without labeled responses.
Clustering is the most common unsupervised learning technique. It is used for exploratory
data analysis to find hidden patterns or groupings in data. Applications for cluster
analysis include gene sequence analysis, market research, and object recognition.
For example, if a cell phone company wants optimize the locations where they build cell phone
towers, they can use machine learning to estimate the number of clusters of people relying on
their towers. A phone can only talk to one tower at a time, so the team uses clustering algorithms
to design the best placement of cell towers to optimize signal reception for groups, or clusters,
of their customers.

6. APPLICATIONS
Machine learning enables organisations to
analyze complex data automatically at scale and
with tremendous accuracy. It gives organisations
the insight they need to make data-driven
decisions about their operations.
However, machine learning algorithms need to be
taught and trained[AC1] to deliver this insight.
For example, when exposed to a large data set,
the machine can detect patterns and use historical
and real-time data to determine the best course of
action or procedure that will deliver the
best result in the shortest possible time.