In 1959, Arthur Samuel coined the term “machine learning“. He was a pioneer in Artificial Intelligence and computer games, defining Machine Learning as a “field of study that allows computers to learn without being explicitly programmed.”
In simple terms, Machine Learning is an application of Artificial Intelligence (AI) which enables a program (software) to learn from the experiences and improve their self at a task without being explicitly programmed. It enables a computer system to make predictions or take some decisions using historical data.
Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data. These predictions could include determining whether a piece of fruit in a photo is a banana or an apple, detecting people crossing the road in front of a self-driving car, determining whether the word book in a sentence refers to a paperback or a hotel reservation, determining whether an email is spam, and accurately recognizing speech to generate captions for a YouTube video.
The main difference between this and regular computer software is that no human developer has written code to tell the system how to detect the difference between a banana and an apple. Instead, a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.