In 1959, computer gaming and AI pioneer Arthur Samuel coined the term at IBM. This is a field of computer science that makes use of statistical techniques to give computer systems the ability to learn without being explicitly programmed. This comes through a quest for artificial intelligence- as they say, necessity is the mother of invention. Many researchers like to claim this is the best way to progress toward human-level AI. With machine learning, we build algorithms with the ability to receive input data and use statistical analysis to predict output while updating output as newer data become available.

We often make use of techniques like supervised, semi-supervised, unsupervised, and reinforcement learning to give machines the ability to learn.

What is Machine Learning?

Machine Learning is the scientific study of algorithms that involves usage of statistical models that computers utilize to carry out specific tasks without any explicit instruction. It relies on patterns and other forms of inferences derived from the data.

Machine Learning algorithms are built on top of a mathematical model that makes use of a sample data known as “training data” for making decisions without any explicit programming.