What is Machine Learning?

What is Machine Learning?

As all of us is very much clear about the leaning concept of humans, they learn from their past experiences. But can we expect the same from computers or any machine to learn itself from the given raw data and past experiences? Thereby the concept of machine learning came into existence.

Machine learning is a subset of artificial intelligence that learns through the raw data and past experiences without being actually programmed explicitly, to give some sense to the data exactly in same manner as humans can do. In other words we can say that ML is a field of Computer Science that deals in extracting out some sensible data on being processed by some ML algorithms. Machine learning was introduced by Arthur Samuel in 1959.


“Machine learning uses statistical tools on data to output a predicted value. It is an application of artificial intelligence that provides the system with the ability to learn and improve from experience without being explicitly programmed automatically”.

What is the need of Machine Learning?

Nowadays, humans have become more advanced and work quite intelligently, especially the way they handle difficult problems and solve them. While on the other hand there is AI which is still undergrowth and has not beaten the human intelligence yet. And so, machine learning is needed for decision making on the basis of some raw data in an efficient manner at a large scale.

As of now, the developers are much more into developing technologies like artificial intelligence, deep learning, and machine learning to extract some information from the given data and performs different algorithm to solve some actual real-world problems especially on a huge scale working as a helping hand to the organization. It can also be known as data-driven decision making. The decision making does not require any programming logic, rather the driven data can be used itself. And for that, it does require human intelligence. Also, the human itself is not enough to solve the real-world problem at a huge scale. So this is when machine learning is needed. As more the data, better the model and higher would be its accuracy.

Working of Machine learning

In traditional programming, we used to provide the data and program, and the computer is used to generate the output.


Whereas, in the case of machine learning, we used to provide data as well as predicted output to the machine, and it learns from the data, find the hidden insights, and creates a model. It takes the output data and repeats its training and growing accordingly so that the model gets better with time on getting trained with new data or the output data.

Applications of Machine Learning

Machine learning is growing so rapidly that in the golden era of AI, where ML is incorporated in our day to day life, and we did not even acknowledge it such as; Alexa, Google maps, Google Assistant and Google maps. Some other real-world applications are enlisted below:-

  1. Emotion analysis
  2. Image Recognition
  3. Speech Recognition
  4. Trading of Stock Market
  5. Fraud detection and its prevention
  6. Product suggestions on online shopping apps
  7. Weather forecasting
  8. Traffic prediction
  9. Friends suggestion on social media apps
  10. Diagnosis of medical issues

Machine Learning vs. Traditional Programming

Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed. Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.

Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.

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