Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F24%3A43926262" target="_blank" >RIV/62156489:43110/24:43926262 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning

Popis výsledku v původním jazyce

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from data and make decisions or predictions without being explicitly programmed. It has become a crucial technology in many industries, from healthcare and finance to entertainment and self-driving cars. At its core, machine learning involves creating algorithms that can analyze and learn patterns from large datasets, enabling systems to improve their performance over time. The goal is for machines to recognize these patterns and use them to predict future outcomes or make decisions on new, unseen data. Machine learning models can be applied to a wide variety of tasks, including classification, regression, clustering, and anomaly detection. For instance, in a classification task, a machine learning model might be used to classify emails as spam or not spam based on patterns it has learned from a training dataset. In a regression task, the model might predict continuous values, such as stock prices or the temperature for the next day, based on historical data. To develop effective machine learning models, it&apos;s important to understand the underlying data and the methods used for training and evaluation. Data preprocessing is a crucial step to clean and prepare the data for modeling, and model evaluation metrics such as accuracy, precision, and recall are used to assess how well the model performs. Understanding these concepts and techniques will lay the foundation for exploring more advanced topics in machine learning.

Název v anglickém jazyce

Machine Learning

Popis výsledku anglicky

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from data and make decisions or predictions without being explicitly programmed. It has become a crucial technology in many industries, from healthcare and finance to entertainment and self-driving cars. At its core, machine learning involves creating algorithms that can analyze and learn patterns from large datasets, enabling systems to improve their performance over time. The goal is for machines to recognize these patterns and use them to predict future outcomes or make decisions on new, unseen data. Machine learning models can be applied to a wide variety of tasks, including classification, regression, clustering, and anomaly detection. For instance, in a classification task, a machine learning model might be used to classify emails as spam or not spam based on patterns it has learned from a training dataset. In a regression task, the model might predict continuous values, such as stock prices or the temperature for the next day, based on historical data. To develop effective machine learning models, it&apos;s important to understand the underlying data and the methods used for training and evaluation. Data preprocessing is a crucial step to clean and prepare the data for modeling, and model evaluation metrics such as accuracy, precision, and recall are used to assess how well the model performs. Understanding these concepts and techniques will lay the foundation for exploring more advanced topics in machine learning.

Druh

B - Odborná kniha

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

ISBN

978-80-558-2228-0

Počet stran knihy

307

Název nakladatele

Univerzita Konštantína Filozofa v Nitre

Místo vydání

Nitra

Kód UT WoS knihy

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