What is machine learning?
Machine learning is a mechanism that involves data analysis by utilizing automated analytical model building. It works on the chief idea that systems are capable of learning from data, identify odd patterns and make profound decisions with little to no human intervention.
How did machine learning evolve?
With the advancement in novel computer technologies, machine learning has modified and it’s not similar to how it used to appear in the past. It came into existence from the age-old theory that computers have the ability to learn some particular functions without having to program, the researchers in the field of AI were keen to know whether computers could learn from existing data or not. It is noticed that when models are exposed to new forms of data, then they have independent adaptability, which makes the aspect of machine learning quite important. Moreover, they get to learn from previous computations, for producing decisions that are reliable, repeatable and provide surefire outcomes. It has gained new momentum with the ongoing trends and advancements, it’s still an old concept in science.
Various machine learning algorithms have worked in the past, but the recent development is having the capability to automatically apply complicated mathematical calculations to big data, and that too repeatedly, at a faster pace than ever. Some examples of machine learning applications can be stated as follows:
Importance of machine learning:
More and more researchers are keen in the branch of machine learning because of the same reason that pulled them towards data mining and Bayesian analysis. Machine learning involves increasing the volume and accuracy of data, cheap and powerful computational processing and better yet affordable storage of data. Therefore, it gives rise to the strong possibility of quicker and automated models that perform analysis on bigger and complex datasets, enables faster delivery and brings effective outcomes, even on a large scale. Certain data preparation capabilities, basic and advanced algorithms, automotive and iterative processes, scalability and lastly ensemble modeling contributes to a good machine learning system.
Use of machine learning in different sectors of economy: