supervised unsupervised learning techniques de pandey pallavi (5 resultados)

Taschenbuch. Condición: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch.

paperback. Condición: New. New. book.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Pallavi PandeyDr. Pallavi Pandey is MTech in CSE from Jamia Hamdard and PhD from IGDTUW, Delhi. She is currently associated with K. R. Mangalam University, Sohna Road, Haryana as assistant Professor and Head of the department of Comp.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.