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Supervised Learning
              Supervised learning is a type of machine learning where the model learns from labelled data, meaning the input data
              is accompanied by the correct output. In supervised learning, the algorithm learns to map input data to output labels
              based  on  sample  input-output  pairs  provided  during  the  training  phase.  The  goal  is  to  learn  a  mapping  function
              from input variables to output variables so the model can make predictions on unseen data. Examples of supervised
              learning algorithms include linear regression, logistic regression, decision trees, Support Vector Machines (SVM), and
              neural networks.
              This approach is widely used in various real-world applications. For instance, in email filtering, supervised learning
              algorithms can classify emails as spam or non-spam based on labelled data. In finance, supervised learning models
              predict  stock  prices  or  assess  credit  risk.  In  healthcare,  these  algorithms  help  to  diagnose  diseases  by  analysing
              patient data with known outcomes, improving both accuracy and efficiency in medical predictions.

                                Labelled Data

                                                                       Prediction                  Carrot
                                                                                                   Bell
                                                                                                   Pepper
                                                     Model Training                                 Tomato

                                  Labels


                                  Tomato
                                          Bell
                            Carrot       Pepper                          Test Data


              Unsupervised Learning
              Unsupervised learning is a type of machine learning where algorithms are used to analyse and cluster unlabelled
              datasets. In unsupervised learning, the algorithm tries to find hidden patterns or structures in the input data without
              explicit guidance. The goal is to explore and discover inherent structures or relationships within the data, such as
              clusters,  associations,  or  anomalies.  Examples  of  unsupervised  learning  algorithms  include  k-means  clustering,
              hierarchical clustering, Principal Component Analysis (PCA), and autoencoders.
              This approach is useful in various real-life applications. For example, in market segmentation, unsupervised learning
              can identify distinct customer groups based on purchasing behaviour, helping businesses tailor marketing strategies.
              In genomics, these algorithms can uncover patterns in DNA sequences, aiding in understanding genetic influences
              on diseases. In cybersecurity, anomaly detection algorithms can identify unusual patterns of activity, helping to detect
              potential security breaches or fraudulent behaviour.


                             Input Raw Data                                            Outputs



                                                  Interpretation             Processing






                                                                 Algorithms



                             Unlabelled Data




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