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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.

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