Explain the difference between Supervised and Unsupervised Learning

Supervised and Unsupervised Learning

The supervised algorithm has both independent dependent variables (target variable) which are implemented by classification and regression techniques. Unsupervised algorithm has only an independent variable that is implemented by the clustering technique.

In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes .

Example : Face recognition

  1. Supervised learning : Learn by examples as to what a face is in terms of structure, color, etc so that after several iterations it learns to define a face
  2. Unsupervised learning : since there is no desired output in this case that is provided therefore categorization is done so that the algorithm differentiates correctly between the face of a horse, cat or human (clustering of data)

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