Introduction to Machine Learning
 /  Introduction to Machine Learning

Machine learning is actively being used today,perhaps in many more places then you would expect.
it lies at the intersection of computer science, engineering, and statistics and often appears in other disciplines.
Topics to be covered:-

  • What is machine learning?
  • Examples in Real life.
  • types of machine learning algorithm.

What is Machine learning..?
Machine learning is a subfield of computer science that is concerned with building algorithms to be useful,rely on a collection of examples of some phenomenon. These example can come from nature, be handcrafted by humans or generated by another algorithm.

Machine learning (ML) is the scientific study of Algorithms and statistical models that computer systems use to progressively improve their performance on specific task.

Machine learning can also be defined as the process of solving a practical problem by 1) gathering a dataset, and 2) algorithmically building a statistical model based on that dataset. That statistical model is assumed to be used some how to solve the practical problem.

kindly watch this small animated video for your clarification.


There are various real life machine learning based example we come across everyday. Let’s have a look at how do it works and help us ease our work.
You check some email,and without you noticing hit the spam filter catches unsolicited ads for pharmaceuticals and places them in spam folders.
You send the birthday card to your friend, and a machine at the post office recognizes your  handwriting to direct the mail to the proper delivery truck.
it is already being used in our daily lives even though you may not be aware of it.
For more real live examples please visit these links..

in all of the  mentioned scenarios machine learning is present .Companies are using it to improve business decisions, increase productivity, detect disease, forecast weather and do many more things.

Machine learning Algorithms:-

  • Supervised learning
  • Unsupervised learning

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