What is Machine Learning in simple terms?

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What is Machine Learning in simple terms?

Machine learning (ML) is starting to reshape how we live, and it’s time we understood what it is and why it matters.

Two important breakthroughs led to the emergence of Machine Learning with the speed it currently has.

One of these was the realization – credited to Arthur Samuel in 1959 – that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored and made available for analysis.

What is Machine Learning

Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.

Definition of Machine Learning

Two definitions of ML can be considered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.

Tom Mitchell provides a more modern definition: “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.”

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

Traditional Modelling Vs Machine Learning

Unlike traditional model, with Machine Learning, computer develop a model by analyzing data, instead of a handcrafted model.

So, we have 2 prominent phases:

  1. Learning Phase
  2. Prediction Phase


Traditional Modelling Vs Machine Learning



So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results.


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