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What Makes it Different?

              Reinforcement Learning  is  unique because of its ability to handle situations where  traditional  methods like
              supervised or unsupervised learning may fall short. In these approaches, you typically need a clear understanding
              of the data and the problem you're solving. However, real-world scenarios often involve complex and dynamic
              environments that are not fully understood, and data might be lacking in certain situations. Additionally, the
              environment may change over time, requiring the system to adapt. Reinforcement Learning stands out because it
              doesn't rely on extensive pre-existing knowledge or large datasets, allowing it to learn from interactions with the
              environment and adapt to unforeseen circumstances, making it highly effective in dynamic & uncertain settings.

              Summary of ML Models



                                                      Family of ML Models



                                Supervised               Unsupervised              Reinforcement
                                 Learning                  Learning                  Learning


                                       Determine
                                                               Discover New                Learn by
                                      relationships
                                                                 Patterns              Rewarding Actions
                                     through training
              Difference  between  Supervised  Learning,  Unsupervised  Learning  and  Reinforcement  Learning  is  explained  as
              follows:

               Aspect      Supervised Learning      Unsupervised Learning         Reinforcement Learning
               Nature of  Labelled data with input-  Unlabelled data; no          No labelled data; the model learns
               Data        output pairs.            predefined output labels.     through interaction with the
                                                                                  environment and receives rewards or
                                                                                  penalties for actions.

               Learning    Learns a direct mapping   Learns patterns, clusters, or   Learns to take actions that maximise
               Process     between input and        structure in data without     long-term rewards based on trial and
                           output.                  predefined labels.            error.

               Feedback    Instant feedback based   No explicit feedback, learns   Feedback is delayed and occurs
                           on the correct label     patterns or relationships.    based on the outcomes of actions.

              Sub-categories of Supervised Learning Models

              The Supervised Learning is further categorised as: Classification and Regression. Let us learn about these in
              detail.

              Classification

              Classification  is  a  rule-based  AI  model.  It  is  a  systematic  grouping  of  observations  in  classes,  something  like
              categorising plants, animals in different categories by biologists. In classification you teach the machine to perform
              with labelled data. Testing data is then classified as one of the labels of the training dataset.
              The algorithm is able to determine to which set a given data point belongs to, by means of a classification function
              represented by the dotted line. The model classifies datasets according to the rules given to it.




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