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Название: A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples)
Автор: Murat Durmus
Издательство: Leanpub
Год: 2023-01-28
Страниц: 192
Язык: английский
Формат: pdf (true), epub, mobi
Размер: 10.2 MB
This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on embedding explicit rules and logical statements in the code. ML is at the heart of recent advances in statistical AI and the methodology behind technological achievements such as computer programs that outperform humans in tasks ranging from medical diagnosis to complex games. The recent surge of interest in AI is largely due to the achievements made possible by ML. As the term “statistical AI” suggests, ML draws on statistics and probability theory concepts. Many forms of ML go beyond traditional statistical methods, which is why we often think of ML as an exciting new field.
Автор: Murat Durmus
Издательство: Leanpub
Год: 2023-01-28
Страниц: 192
Язык: английский
Формат: pdf (true), epub, mobi
Размер: 10.2 MB
This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Machine Learning (ML) refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on embedding explicit rules and logical statements in the code. ML is at the heart of recent advances in statistical AI and the methodology behind technological achievements such as computer programs that outperform humans in tasks ranging from medical diagnosis to complex games. The recent surge of interest in AI is largely due to the achievements made possible by ML. As the term “statistical AI” suggests, ML draws on statistics and probability theory concepts. Many forms of ML go beyond traditional statistical methods, which is why we often think of ML as an exciting new field.