Machine learning (ML) is a branch of computer science empowering computers to learn without being specifically programmed. The background for machine learning was laid in 1990s with the concept of neural networks. This was achieved by designing electronic circuits to imitate the way neurons in the human brain connect.
A human brain consists of billions of neurons. Each neuron is connected to hundreds of other neurons to effectively discharge the functions of brain. Machine learning concept is similar to this function. Earlier computers were programmed to perform various tasks. The actions of computers were based on various logics designed by the programmer. Machine Learning lets the system to find out the solution from scratch. Deep learning is a method used by systems to learn and this is achieved by hierarchically arranging layers of neurons to distinguish objects. Electronic circuits learn and achieve their goals by learning repeatedly from past experience. They acquire the capability to differentiate one animal from another or one picture from another through various methods of rewards and punishments. They identify patterns in complex problems, compare with learnings and make predictions. The computing power of the system and underlying algorithms only are their limitations.
The terms Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably. ML is actually the brain behind the AI. During initial phases, a computer programmed to play chess was considered as an example of artificial intelligence. Now, there are machines capable of solving highly complex problems through artificial intelligence and now a section of scientists believes that systems have become intelligent enough to rule the planet. Advanced programme skills, availability of powerful computing capability at cheap prices and accessibility to huge volume of data to learn have aided the progress in the field. This makes machine to learn better and perform better than humans.
There are machines capable of predicting the root cause of diseases, defeating chess champions, writing news articles and composing music. Even then self-learning has not reached its pinnacle. Machine Learning is used in circumstances where designing and programming algorithms with good performance is impractical.
The term machine learning was coined in 1959 by Arthur Samuel, a scientist in IBM. ML is closely linked to computational statistics and mathematics to derive its strength in prediction. Data mining uses this potential. There are also scientists who point out that effective machine learning is difficult because of practical difficulties in finding out patterns, which is the key behind machine learning.