A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

Жанр: АльтернативаИсполнитель: Muller AugustusАльбом: Machine Learning ExperimentsГод записи: 2022 годСостав набора: Аналоговая пластинкаКоличество носителей: 1Упаковка: СтандартнаяИздатель: City Slang

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Научитесь создавать модели ML и обучать нейронные сети. Освоите анализ данных и в конце курса выберете одну из специализаций: обработку естественного языка или Computer Vision. Через 9 месяцев сможете устроиться ML-инженером, параллельно продолжите проходить курс и дорастёте до уровня Middle.

Вы освоите навыки анализа данных с помощью Python, оптимизируете свою работу и будете ценным сотрудником для любой digital-компании.Разберем: от Python до основ Machine Learning

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Вы станете экспертом в анализе данных с помощью Python, оптимизируете свою работу и будете ценным сотрудником для любой digital-компании. Изучите: от Python до Power BI и Machine Learning

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Вы поймете, как систематизировать большие данные с помощью Machine Learning и научитесь работать с рекомендательными системами. Освоите такие инструменты как Python и Hadoop, а также узнаете особенности библиотек данных Pandas, Numpy, Matplotlib, Plotly.