USING MACHINE LEARNING FOR DATA PROCESSING ON MICROCOMPUTERS - HNATIUK, OLEKSANDR; SMOLINSKYI, YURII; VINTU, ARTUR (Herald of Khmelnytskyi National University. Technical sciences, 2024)
Machine learning is a data analysis method that automates the construction of an analytical model. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience. It is closely related to computational statistics, which makes predictions based on statistical data collected by a computer. It is sometimes confused with data mining, but that is more focused on analyzing intelligence data, while machine learning contains complex algorithms that are used mainly for prediction, where machine learning concentrates on predicting based on already known attributes obtained through training data, then data mining focuses more on finding unknown attributes in any data. attributes in any data. Machine learning applications may be associated with spam filtering, optical character recognition, and search engines. Machine learning uses data to determine which algorithm is best for generating results based on the quantity, quality, and nature of the data. This data is then used for intelligent analysis in various ways, such as recommendation systems such as similar products on eBay, personalized content on google plus pages, video ads on sites such as YouTube, and last but not least, suggestions from friends on Facebook. Machine learning and artificial intelligence can be used to solve business problems, namely: Automating repetitive tasks such as data entry, document processing and customer service. This frees up employees' time for more complex work. Optimization of business processes. ML/AI can analyze data to identify inefficiencies and optimize business processes, increasing company productivity. Service personalization. ML/AI helps analyze customer data to provide them with personalized offers, recommendations and support. Automation of answers to questions and anticipation of customer needs. Detection of fraudulent transactions and cyber security. Risk management.
Keywords: machine learning; analysis; data; algorithm