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A Brief Introduction to Machine Learning

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A Brief Introduction to Machine Learning

Machine learning programs are capable of improving based on experience. In this brief intro, learn a little more about what ML is and how it differs from other programs.

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Before jumping directly into what exactly machine learning is, let's starts with the meaning of the individual words. This may seem obvious, but it's best to start from the very beginning.

  • A machine is a tool containing one or more parts that transform energy. Machines are usually powered by chemical, thermal, or electrical means, and are often motorized.
  • Learning is the ability to improve behavior based on experience.

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 machine is seeking to improve. It can be something like prediction, classification, clustering, etc.
  • Experience E can be training data or input data through which the machine tries to learn.
  • Performance P can be some factor, like improvements in accuracy or new skills that the machine was previously unaware of.

Machine Learning1

Machine learning itself contains two main components: the learner and the reasoner.

  • Input/experience is given to the learner, who learn some new skills.
  • Background knowledge can also be given to the learner for better learning.
  • With the help of input and background, the knowledge learner generates the model.
  • The model contains information about what's been learned from the input 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 solution.
  • The solution/answer can be improved by adding additional input/experience.
  • And so the cycle continues.

How Machine Learning Differs From Standard Programs

In machine learning, you feed the computer the following things:

  • Input (experience)
  • Output (output corresponding to inputs)

And get the model/program as output. With the help of this program, you can perform some tasks.

On the other hand, in a standard program, you feed the computer the following things:

  • Input
  • Program (how to process the input)

And after that, you get the output. 

Here's a diagram to help you understand:

Machine Learning

And that's it for this brief introduction to machine learning.

Your machine learning project needs enormous amounts of training data to get to a production-ready confidence level. Get a checklist approach to assembling the combination of technology, workforce and project management skills you’ll need to prepare your own training data.

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