Machine learning is a discipline in artificial intelligence wherein machines are empowered to explore data from previous experiences and make predictions with the least human intervention. It involves systems that are built to learn and improve on the past. Machine learning focuses on improving computer software to allow it to access information and learn independently.
How machine learning works
Machine learning is a concept drawn from the functionality of the human brain. Just like the brain receives information through teaching, machine learning also relies on input. The software is also taught through domains, data and aids such as graphs that enable it to understand the link between these phenomena. These entities must also be defined, much in the same way humans are introduced to various academic subjects.
The process of machine learning (ML) starts with the basic steps, including examples, direct experience or data observation. ML identifies various patterns in the learning process, stores them in its memory and later refers to them according to the examples provided. ML was invented to give computers the autonomy to learn on their own so that little, if any, human intervention is needed.
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Why machine learning is used in computer science
Machine learning has been around for many years, and the term was first introduced by an IBM scientist known as Arthur Samuel. He is also among the pioneers of computer gaming and AI. If you enroll for an online master’s of computer science at an accredited university like Worcester Polytechnic Institute, you will likely learn about this veteran, who also invented a program for playing checkers, as part of your studies. These programs are a great choice for students who want to work in the field but cannot attend courses in person.
ML is a critical aspect of the modern world, and it helps achieve many goals in computer science. For example, it allows very complex problems to be solved at a high speed. This ability goes beyond the capacity of the human mind because of numerous duplications. Machines can also be used to trace various patterns in data sets and automate routine processes.
In computer science, data is crucial. ML is used to build algorithms that lead to mathematical models created out of sample data. This data is referred to as training data, and you can use it to make decisions without programming.
Machine learning is a critical component of AI systems. It is the foundation upon which the systems are automated to provide solutions. Companies can use ML to fill in gaps where human capabilities are limited. Computer science has facilitated the spread of machine learning applications worldwide, such as speech recognition, self-driving cars and chatbots.
Other sectors that have adopted machine learning
Other sectors of the economy have also benefited from machine learning. For example, in finance, fintech firms, brokerages and banks use algorithms to disseminate financial advisory services to investors. In America, many banks have started to use chatbots to automate customer support.
ML has also been deployed in healthcare to speed up the process of identifying cures and treatments. This sensitive sector requires minimal human error, and machine learning has been valuable for automating routine procedures. Fraud detection agencies also use this concept to analyze transactions and flag those that may be fraudulent.