Have you ever thought about how Google’s Gmail decides which mail is spam and which is not? Or have you wondered why WhatsApp messenger suggests words frequently used by you while messaging your friends or family members?
Everything around you be it your mobile phone device or any software you use on any device is basically a machine. A machine is nothing but anything that is made to make our day-to-day life tasks easy.
The way we learn from our past experiences in life and make informed decisions about our future actions, machines are also capable of doing the same.
Machine Learning teaches computers to do what naturally comes to humans and even animals. There are certain algorithms also known as machine learning algorithms that are fed to the computers that use computational methods to ‘learn’ information directly from data and form a so-called model. These models are nothing but some mathematical equations that are constructed based on the data received or collected. So these models can also be called mathematical models. Now, who collects the data? It could be the machine itself or it could be us feeding data to the machine after collecting it. The algorithms adaptively improve their performance as the number of data samples available for learning increases.
Machine learning (ML) uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. To get a reliable and realistic prediction we need to have a huge amount of data in hand. Otherwise, the prediction might just come out to be a statistical fluke.
Usually in the process, a part of the data known as the train data, is fed to the ML algorithm to train itself, so that it can build a model on that set of data. The rest, known as the test data, is fed to the algorithm to test the model's validity. There is a target parameter that we wish to predict for future outcomes and there are other parameters that govern that target parameter's outcome.
In a nutshell, ML is a type of artificial intelligence where machines learn from past experiences and make constructive future decisions.