What Is Machine Learning?

According to Tom Mitchell, machine learning is:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

In this definition:

  • Task T is what computer is seeking to reinforce. It can be whatever like prediction, classification, clustering, and so forth.
  • Experience E may also be coaching knowledge or input data through which the machine tries to gain knowledge of.
  • Performance P can be some aspect, like upgrades in accuracy or new skills that the laptop was beforehand blind to.
  • Machine learning itself involves two important add-ons: the learner and the reasoner.
  • Input/experience is given to the learner, who be trained some new knowledge.
  • Background knowledge may additionally accept to the learner for better finding out.
  • With the support of input and background, the knowledge learner generates the model.
  • The mannequin information about what's been learned from the enter and experience.
  • Now, the problem/task (i.E. Prediction, classification) is given to the reasoner.
  • With the help of trained model, the reasoner tries to generate the answer.
  • The solution/answer can be improved by using adding extra enter/expertise.
And so the cycle continues.

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