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- Dictionarymachine learning
noun
- 1. the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data: "the application of machine learning to biological databases has increased"
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Mar 27, 2024 · Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models that enable machines to perform tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuations.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. How does machine learning work?
May 25, 2023 · Machine Learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Learn about the types and algorithms of Machine Learning with examples.
- 16 min
Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [1] .
Learn what machine learning is, how it works, and why it is important. Explore the different types of machine learning, such as supervised, unsupervised, and reinforcement learning, with examples and applications.
Machine Learning, often abbreviated as ML, is a subset of artificial intelligence (AI) that focuses on the development of computer algorithms that improve automatically through experience and by the use of data.
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Nov 8, 2022 · Machine learning is a subfield of artificial intelligence in which systems have the ability to “learn” through data, statistics and trial and error in order to optimize processes and innovate at quicker rates.
