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Название: 40 Algorithms Every Data Scientist Should Know: Navigating through essential AI and ML algorithms
Автор: Jürgen Weichenberger, Huw Kwon
Издательство: BPB Publications
Год: 2025
Страниц: 588
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to Deep Learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application. This book offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML. This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI.
Автор: Jürgen Weichenberger, Huw Kwon
Издательство: BPB Publications
Год: 2025
Страниц: 588
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to Deep Learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application. This book offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML. This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI.