Computers facilitate the activities in life. They till now perform in accordance with the programme and applications. Machine learning breaks new ground. Here the computer performs important tasks by generalising from the available data and examples. It is based on algorithms which can learn from data, with no dependence on rule-based programmes. Machine learning came on the scene in the late 1990s with steady advances in digitisation and economical computing power.

Machine learning is related to artificial intelligence ( AI ). AI means exercising those skills by a computer which requires human intelligence. To illustrate, humans do decision making and visual perception. If computers do it, it is called AI. Computers can be trained to do things which cannot be programmed in advance. Everyday illustrations of machining learning are search engines, Apple’s photo tagging and G-mail’s spam filtering. The basic aim of machine learning is to generalise on the basis of examples in the training set.

Here there are two components of mechanisation in ML. A suitably programmed machine does the classification and prediction. Secondly, the creation of classifier itself is highly mechanised, with the least human input.

AI and ML are not the same. AI is a broad term. Here the computer comes to solve the problems on its own. The information required for solution is coded and AI uses this data to arrive at the solution. ML goes a step beyond. It generalises information from the large data sets and extrapolates and detects patterns to apply the information to new solutions and actions.It is obvious that AI and ML are highly interdependent.

ML enables us to understand the dynamic markets. Organisations do use historical data but they also have to predict behaviour or results. ML gives actionable insights. It makes predictions and finds solutions for business problems — customised recommendations for customers, future performance of employees, forecasting customer loyalty.

ML algos compare new cases to large data bases of similar cases in the past. It is a useful tool for decision makers. Despite this, only humans have critical faculties. ML is made available by cloud providers like Amazon Web Services and Microsoft Azure. These are cloud-based ML services.