After offloading physical efforts to machines, we have also started offloading some of these mental efforts. We have successfully offloaded all possible calculations and information. Today, very few of us waste our effort of calculating and remembering stuff. We have computers and mobile phones that easily take care of this job. The next we are attempting is the ability to learn from surroundings, and use the learnt information to analyse events and take decisions based on what we have learnt. Machine learning deals with this aspect of artificial intelligence. This is not a new branch of computing. It has been several decades since we have worked on this science. But now, it has gained a lot of momentum because we have seen a lot of commercial value of its application.
Essentially, machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. In more formal terms, a computer program is said to learn from experience with respect to some task and performance measure, if its performance at the tasks improves with the experience.
It's not as complex as it sounds! In order to understand machine learning, we should try to understand how humans learn. The most commonly quoted example - probably the most important learning we went through is standing up. A child is not able to stand up on its feet. There are hundreds of muscles in the body. He has no idea about which muscle leads to a given action. All that he knows is that everyone around him is able to stand up and walk. So he tries to follow, and falls several times. Subconsciously, his mind observes each such event. It notes the events and actions through all his attempts to stand up. The mind subconsciously makes a note of how a particular muscle in the legs seems to impact his position while he stands. Each attempt to stand up gives a fresh set of observations to the mind - on events that kept him standing and events that led to a fall; and the effect of various muscular movements on either. Based on this, the mind builds up a map of muscular movements against its effect on the position of the body.
The mind just "learns" from a generalization based on several consecutive observations. The process matures as this set of observations build up and one day, he is able to stand up, walk and run... None of these observations used any kind of calculations or measurements. The child does not need any knowledge of mechanical engineering to define the stability of the body. No knowledge of anatomy is required to identify which muscle in the body impacts a particular joint. Everything is just "learnt" from data gathered from observations over several events. Each such observation adds to what was learnt.
Now consider the state when the same person gets into a roller coaster. His observations are entirely different. The same fall is simulated again and again. The jerky motion tosses his body around. At this time, the machine moves in in various directions, in high speed - forcing him to use his muscles to hold on. The mind again tries to learn from these events. But, this gives an entirely different set of observations. These are quite different from the ones that the mind had gathered while it was learning to stand up. This confuses the mind. Because it is forced to rework what it had learnt. And, when the person gets off the roller coaster, he is not able to stand still - what the mind had learnt years ago is now disturbed by the recent observations. This confusion remains for a few minutes and as the mind continues to observe, it notices that what it had learnt before continues to hold. Then it just pushes aside the recent set of observations as an exception - possibly specific to the roller coaster - and carries on. If he is a regular at the roller coaster, he is pretty stable on the ride as well as on coming down. The mind soon identifies two different sets of observations - specific to the ground and the roller coaster.
Machine learning is attempting to replicate, in a very limited scope, this behavior of the mind - the mind's ability to classify and learn from events. The mind's ability to decide and conclude based on mere observation - without any need to know the low level details.