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Unsupervised Learning Algorithm and Its Uses
Clustering and Dimensionality Reduction are common algorithms in unsupervised learning.
Clustering is a machine learning approach where the machine partitions the dataset into different clusters or
categories based on similar characteristics. The uses of clustering algorithms are:
Targeted
Marketing
Recommender Customer
Systems Segmentation
Unsupervised
Clustering Learning
Dimensionality Reduction is a technique used to reduce the number of features or variables in a dataset while preserving
the most important information. It is particularly useful when dealing with high-dimensional data, where the number
of features is large relative to the number of samples. Dimensionality Reduction methods aim to simplify the dataset,
making it easier to visualise, analyse, and model while also reducing computational complexity.
Advantages of Unsupervised Learning:
• Unsupervised learning is used for more complex tasks as compared to supervised learning because, in unsupervised
learning, we don't have labelled input data.
• Unsupervised learning is preferable as it is easy to get unlabelled data in comparison to labelled data.
Disadvantages of Unsupervised Learning:
• Unsupervised learning is intrinsically more difficult than supervised learning as it does not have corresponding output.
• The result of the unsupervised learning algorithm might be less accurate as input data is not labelled, and algorithms
do not know the exact output in advance.
Reinforcement Learning
Reinforcement Learning
Follow Trial and Error
Reinforcement Learning (RL) is a type of machine learning technique that method
enables an agent to learn in an interactive environment by trial and error using
feedback from its own actions and experiences. The agent takes actions and
observes the outcomes, receiving feedback in the form of rewards or penalties.
Over time, through repeated trials and adjustments to its strategy, the agent
refines its decision-making process to achieve better performance and maximise
its cumulative reward. This iterative approach allows the agent to learn optimal
behaviours without explicit instructions or supervision.
Examples of Reinforcement Learning: chess game, text summarisation
Regression
Regression is a Supervised Machine Learning algorithm used to analyse the relationship among dependent
variable (target) and independent variable (predictor). The objective is to determine the most suitable function that
characterises the connection between these variables.
Machine Learning Algorithms 325

