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Learning-based Approach








                                                                     Model

                                                                 Model Training




                               Unlabelled Data                                       Clustering on the Basis of Size

              A  learning-based  approach  in  AI  allows  the  machine  to  train  on  data  and  adapt  its  model  dynamically.  It
              modifies itself based on data changes, ensuring adaptability and handling exceptions effectively.
              For example, a learning-based product recommendation system is an AI model used by e-commerce platforms
              like Amazon or Flipkart to suggest products to users based on their behaviour, preferences, and purchase
              history. Unlike rule-based systems that suggest predefined items, this approach learns patterns and adapts to
              users' interests over time.
              The system is trained using a large dataset of user interactions, that includes browsing history, purchase history,
              search queries and reviews/rating. The trained model analyses a user’s current activity in real time and matches it
              with patterns learned during training. Using machine learning algorithms, the recommendation system identifies
              shifts in user interests or seasonal preferences. This learning-based recommendation system enables e-commerce
              platforms to offer a personalised shopping experience, boosting user satisfaction and business revenue.
              The Learning-based Approach can further be divided into three sections:

                                                        Learning-based Approach




                       Supervised Learning              Unsupervised Learning              Reinforcement Learning


              Supervised Learning


              Supervised Learning is a type of machine learning where a model is trained
              on a labelled dataset. A labelled dataset is the information which is tagged
              with  identifiers  of  data.  Labels  are  the  key  component  in  supervised
              learning,  as they guide the learning  process. A label is  an information
              that can be used as the tag for data. For example, students in a class are
              evaluated based on their performance in exams and assignments. Their
              performance is categorised into labels such as "Outstanding," "Very Good,"

              "Satisfactory," or "Needs Improvement."
              Supervised learning is like, how a teacher helps students learn. The teacher provides clear examples and guidance
              (training data) to teach concepts. Later, the teacher tests the students' understanding with new questions (testing
              data). Similarly, a supervised learning model uses labelled training data to learn patterns and then applies this
              knowledge to make predictions on unseen data, improving its accuracy over time.


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