Supervised and unsupervised learning techniques for healthcare
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Machine learning (ML) is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and improve their performance on tasks without explicit programming. At its core, ML involves the utilization of data to recognize patterns, make predictions, and inform decision-making. It encompasses a variety of techniques, from supervised learning, where models are trained on labeled data, to unsupervised learning, where algorithms identify hidden patterns within unlabeled data. ML applications span diverse domains, incl...
Machine learning (ML) is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and improve their performance on tasks without explicit programming. At its core, ML involves the utilization of data to recognize patterns, make predictions, and inform decision-making. It encompasses a variety of techniques, from supervised learning, where models are trained on labeled data, to unsupervised learning, where algorithms identify hidden patterns within unlabeled data. ML applications span diverse domains, including image and speech recognition, natural language processing, healthcare diagnostics, and predictive analytics, fundamentally reshaping how computers can autonomously acquire knowledge and adapt to changing environments.
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