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Unsupervised Machine Learning

 Unsupervised learning is the learning in which there is only input variables and not output variables corresponding to that input variable. Unsupervised learning is a type which is used to draw conclusion from dataset containing input data without any label. An unsupervised learning means the case of data in which there is no expected output related to that input data . The algorithm of unsupervised learning finds the pattern which depends on the values of data inputted.

One of the most important application of unsupervised learning is CLUSTERING in which inputs of same category or types are kept together in order to form the cluster of input data.  The above picture represents the process of clustering in which different varieties of fruits are grouped together as input and the output is in cluster form. In the unsupervised learning, the algorithm itself is not capable of adding label which means that it cannot identify the meaning of the output given.

It is called unsupervised learning because in this type there is no conformation of correct answer and there is no teacher as algorithm are left to find the pattern by their own in the data .

The model is trained using unsupervised learning algorithm. The clusters are formed on the basis of this training only.

 

Some of the popular  uses of Unsupervised Learning are –

3.2.1.Banking sector- It is used in banks for part customers accoerding to their behaviour by many surveys to divide them in clusters.

3.2.2.Healthcare-  This technique is used in MRI and Cityscan process to differentiate between normal and abnormal pictues.

3.2.3.Retail sector- It is used to recommend product to customres based on their past purchases.

 

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