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Unsupervised Learning
            An unsupervised learning approach works on an unlabeled dataset. This means that the data which is fed to the
            machine is random and there is no know how available about it to the trainer.
            These learning models are used to identify trend, pattern and relationship in the data which is fed into it. In this
            model the major features are identified by the machine, which helps the user in understanding the data.
            For example, in the data of 100 cat images, if you want to understand some pattern in the data, you would need
            to feed this data into the unsupervised learning model and train the machine. Once trained, the machine would
            identify patterns in the data. These patterns might already be known to the user, like colour or size, or something
            unusual about the cats.

            Unsupervised Learning can further be divided into:
            Clustering

            It is a machine learning approach where the machine partitions the dataset into different clusters or categories
            based on machine generated algorithms. The data fed to such a model is usually unlabeled or random and thus
            the developer feeds in the data directly into the machine and instructs it to build its own algorithm. The machine
            then forms a pattern or cluster based on training data and groups those that follow the same pattern.


























            The best clustering is the one that minimizes the error. Clustering works on discrete dataset. For example, if you have
            random data of flowers from your garden, since you are unable to find any meaningful pattern amongst them, you
            would feed their data into the clustering algorithm. The algorithm would then analyse the data and divide them into
            clusters according to their similarities based on the trends noticed. The clusters are then given as the output.
            Dimensionality Reduction
            Humans can visualise any figure up to 3-Dimensions only, but according to a lot of theories and algorithms, there
            are various entities which exist beyond 3-Dimensions. For example, in Natural language Processing, the words are
            considered to be N-Dimensional entities, which means that we cannot visualise them as they exist beyond our
            visualisation ability. Hence, to make sense out of it, we need to reduce their dimensions which we do by using
            dimensionality reduction algorithm.

            As we reduce the dimension of an entity, the information which it contains starts getting distorted. For example, if
            we have a ball in our hand, it is 3-Dimensional right now. But if we click its picture, the data transforms to 2-D as an
            image which is a 2-Dimensional entity. Now, as soon as we reduce one dimension, at least 50% of the information
            is lost as now we will not know about the back of the ball.

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