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Unsupervised Learning
              Unsupervised learning is like learning without a teacher. The machine learns through observation and finds patterns
              in data. The system will explore the data and draw inferences from the data set to describe the hidden patterns in the
              unlabelled data. Unsupervised machine learning algorithms are used when the information used to train is neither
              classified nor labelled.

              For example, if somebody gives you a basket full of different fruits and asks you to separate them, you will probably do it
              based on their colour, shape, and size, right? Unsupervised learning works in the same way. As you can see in the image:



                          Raw Data is input                                                   Output
                                                           Algorithm
                                        • Unknown Output
                                        • No Training Data Set





                                             Interpretation            Processing


                                                        Model Training                   Model is trained



              Unsupervised Learning Algorithm and Its Uses
              There is one algorithm of unsupervised learning called clustering. Clustering is a machine learning approach where
              the machine partitions the dataset into different clusters or categories based on machine generated algorithms.  The
              uses of clustering algorithm are:


                                                          Targeted
                                                         Marketing
                                            Recommender                Customer
                                              Systems                Segmentation




                                                                              Unsupervised
                                                         Clustering             Learning






              Reinforcement learning
              Reinforcement learning methods resemble how humans and animals learn. Consider the scenario of teaching new
              tricks to your pet dog. As the dog doesn’t understand English or any other human language, we can’t tell it directly
              what to do. Instead, we follow a different strategy:

                 • We imitate a situation, and the dog tries to respond in many different ways. If the dog’s response is the desired way,
                 we will give it food.
                 • Now whenever the dog is exposed to the same situation, it executes a similar action with even more enthusiasm in
                 expectation of getting more reward(food).
                 • The dog learns “what to do” from positive experiences.
                 • At the same time, the dog also learns what not to do when faced with negative experiences.

              Reinforcement learning algorithms interact with the environment by producing actions and discovering errors or
              rewards.

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