#machinelearning use cases:

1. Supervised
2. Unsupervised
3. Reinforcement
  • It is a type of machine learning, where one guides the system by tagging the output.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
  • Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?

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@ValaAfshar: “#machinelearning use cases: 1. Supervised 2. Unsupervised 3. Reinforcement” open tweet »

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