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u real-time learning, so that all input data is analysed and labelled in the presence of learners.

u learning models where getting labelled data is not available.

Kno
Knowledge Botwledge Bot
Unsupervised Learning is used in banks to analyse data for suspicious transactions that involves identify-
ing patterns or anomalies that could indicate fraudulent activity without relying on pre-labeled examples
of fraudulent and legitimate transactions.

Difference between Supervised Learning and Unsupervised Learning

Supervised Learning and Unsupervised Learning are two primary approaches in machine learning, each serving
different purposes and applied in various scenarios. The main difference between these two approaches are
summarised in the table given below.

Feature Supervised Learning Unsupervised Learning
Definition Learns from labelled data where Learns from unlabelled data to find patterns or
input-output pairs are provided. groupings.

Objective To predict outcomes based on input To discover hidden patterns or intrinsic structures
features. in data.

Data Requirements Requires a labelled dataset to train Requires an unlabelled dataset with no predefined
the model. outputs.

Output Produces predictions that can Produces patterns, clusters, or associations that
be evaluated against predefined require interpretation.
labels.

Example Predicting house prices based on Segmenting customers based on purchasing
features like size and location. behaviour without prior labels.

Knowledge Botwledge Bot
Kno
The ‘Semi-Supervised’ machine learning algorithms combine techniques from Supervised and Unsuper-
vised algorithms for applications with a small set of labeled data and a large set of unlabeled data. In
practice, using them leads to exactly what you would expect, a mix of strengths and weaknesses of both
the approaches.
the approaches.

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