The premise behind learning algorithms is that approaches, methods, and inferences that have proven successful in the past are likely to do so again in the future. These inferences can be straightforward, such "the sun will probably rise tomorrow morning as well" or "because it has been rising every morning for the last 10,000 days." They can be subtle, like "Y% possibility that undiscovered black swans exist since X% of families have geographically separate species with colour variants." Programs that use Machine Learning can do tasks without having them explicitly coded. Computers use available data to learn in order to do specific jobs.
For straightforward jobs given to computers, it is possible to build algorithms that instruct the device how to carry out all the steps necessary to address the issue at hand; no learning is required on the part of the computer. It can be difficult for a human to manually develop the required algorithms for more complex tasks. Helping the computer create its own algorithm may prove to be more effective in practise than having human programmers specify each necessary step.
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