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Today’s technology has advanced enough for the development of AI-enabled applications, which ensure enhanced efficiency without relying heavily on manual programming. Machine learning in app development puts its focus on the creation of applications and programs that are fully data-driven, enabling progressive solutions to problems and issues within the system. With artificial intelligence being an integral part of modern apps, machine learning is changing the way software programs and applications are developed.

Machine learning isn’t an entirely new concept. As a matter of fact, the term machine learning has been around for quite some time, first coined in 1959 by Arthur Samuel. It refers to the science of enabling machines or computers to monitor a system’s status and performance by means of data collection, and to come up with the best ways to solve it without external help. Machine learning is such a crucial aspect of app development as it allows for deeper levels of customization within apps and programs, allowing for the identification and categorization of users according to their needs and at the same time cater to the unique requirements and issues within an organizational system.

Machine learning is also helpful in optimizing search filtering and returning more intelligent search results according to specific parameters and subjects. This technology is also responsible for allowing application owners to know about individual user preferences by means of monitoring purchase patterns and other available details and intelligence from across the system, and sometimes even from across the web. Machine learning app development enables apps to provide recommendations that a particular user will likely want or require based on their activities, online behavior and app usage.

Nowadays, machine learning is being extensively used in a wide range of mobile applications either as part of an application’s artificial language or to provide additional security and filtering capabilities for app users. Machine learning is also widely used for processing large amounts of data and enabling predictive computing.