Parametric Methods
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A parametric approach (Regression, Linear Support Vector Machines)has a fixed number of parameters and it makes a lot of assumptions about the data. This is because they are used for known data distributions. i.e, it makes a lot of presumptions about the data

Non-Parametric Methods
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A non-parametric approach (k-Nearest Neighbours, Decision Trees)has a flexible number of parameters, there are no presumptions about the data distribution. The model tries to "explore" the distribution and thus has a flexible number of parameters. 

Comparision
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Comparatively speaking, parametric approaches are computationally faster and have more statistical power when compared to non-parametric methods

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Hope this cleared your doubts 😊