Our quest to find sentience has led to the discovery of many forms of learning which are beneficial to us. Finding AI itself can be a hard-pressed task. True AI has too many curves and edges we fully cannot bend around and seem to understand and realise.

In our journey through computing, one thing has always been clear; in order for the computer to fully perform that task, it needs to be given commands. Only after the computer has received the commands can it fully execute the task to perfection.

Somewhere along the line, the computers adapted a method, where it did not require commands to complete the task. We have managed to incorporate computers with the ability to learn from data and analytics on a specific task. It progressively improves performance on a specific task, by trying to adjust to standards and ensuring perfection.  It basically involves predictions based on samples and data sets to improve their algorithms consistently. Such dynamic features help it thrive in a world where data is a paramount structure in any corporation or business.

The economy today is reliant on making predictions and their outcome. It is so because the market today is analysed and data is collected on almost any aspect of work. This is a prime reason as to why machine learning is very critical. It can take data from any time period, compile it and run accurate analysis to produce reliable, repeatable decisions and results. It can even cover unseen trends and other unnoticed factors, which can help towards the growth of the department in the future.

Machine learning solutions today are important for growing businesses and emerging start-ups. They require data and a concrete analysis to stay ahead of the curve. If they can predicate the outcome of a few key models in their business, they can utilise that to their advantage.

Its applications are limitless, but its chief ones are in the field of economics. There is a close link to statistics, as machine learning is reliant on the data pattern and recording them. It is, however, different from data mining, which focuses on combing through the data to find previously unknown properties of the said data. There have been similarities and comparisons drawn against each other, partly due to the fact that both deal with deriving results from data.

The difference lays in the fact that, machine learning tries to work on reproducing previously known knowledge. It does this on its own without the help of any input. Data mining whereas focuses on getting aspects of the data which were not discovered before. Machine learning is also useful in optimizing, as it replicates the process until it finds the ideal way to do it. This is a very important requirement for some organizations whose processes may just be repetitive.

The sort of demand for this has led to a rise in the number of machine learning companies around the world. They offer their clients a host of functionality and help them optimize and perfect process all the while getting necessary models for prediction.


The author Luke

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