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Название: Machine Learning: Concepts, Techniques and Applications
Автор: T.V. Geetha, S. Sendhilkumar
Издательство: CRC Press
Год: 2023
Страниц: 478
Язык: английский
Формат: pdf (true)
Размер: 36.5 MB
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining Machine Learning and goes on to explain the basis of Machine Learning algorithms. The mathematical foundations required are outlined along with their associations to Machine Learning. The book then goes on to describe important Machine Learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical Machine Learning has been discussed. An outline of Deep Learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. The R language was basically developed by statisticians to help other statisticians and developers work faster and more efficiently with the data. By now, we know that Machine Learning is basically working with a large amount of data and statistics as a part of Data Science, so the use of the R language is always recommended. Therefore the R language is becoming handy for those working with Machine Learning, making tasks easier, faster, and more innovative.
Автор: T.V. Geetha, S. Sendhilkumar
Издательство: CRC Press
Год: 2023
Страниц: 478
Язык: английский
Формат: pdf (true)
Размер: 36.5 MB
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining Machine Learning and goes on to explain the basis of Machine Learning algorithms. The mathematical foundations required are outlined along with their associations to Machine Learning. The book then goes on to describe important Machine Learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical Machine Learning has been discussed. An outline of Deep Learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. The R language was basically developed by statisticians to help other statisticians and developers work faster and more efficiently with the data. By now, we know that Machine Learning is basically working with a large amount of data and statistics as a part of Data Science, so the use of the R language is always recommended. Therefore the R language is becoming handy for those working with Machine Learning, making tasks easier, faster, and more innovative.