Название: Introduction to Machine Learning with Security: Theory and Practice Using Python in the Cloud, 2nd Edition
Автор: Pramod Gupta, Naresh Kumar Sehgal, John M. Acken
Издательство: Springer
Год: 2025
Страниц: 509
Язык: английский
Формат: pdf (true)
Размер: 16.6 MB

This book provides an introduction to Machine Learning, security and cloud computing, from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.

- Provides broad coverage of AI, Machine Learning and Cloud Computing
- Uses real examples and case studies to demonstrate key topics
- Demonstrates concepts, as well as practical usage

The book takes the students to comprehend and learn the concepts of data science and statistics, thereby setting up their own Machine Learning platform with open-source tools. The intention behind the book is to concentrate more on the usage and application of Machine Learning. The book covers a wide base of techniques, from the simplest and most commonly used algorithms to complex Machine Learning algorithms.

The authors of this book have leveraged their hands-on experience with solving real-world problems using Python and the Machine Learning ecosystem to help the readers gain solid knowledge needed to apply essential concepts, methodologies, tools, and techniques for solving their own real-world problems.

The book aims to cater to readers with varying skill levels ranging from beginners to experts and enable them in structuring and building practical Machine Learning and AI solutions.

This book is appropriate for both advanced undergraduate or master’s students who want to work in this domain, or for individuals working in the area of Machine Learning. It is an excellent resource for those who wish to start learning Data Science and Machine Learning so as to understand and use these powerful techniques in their work area. By the end of the book, readers will have the knowledge on the tools needed to begin their journey in the domain of Machine Learning and Artificial Intelligence. The book will also support students to treat information securely as both AI and ML are intertwined due to data confidentiality, model integrity, and system availability. Solutions such as encryption, hashing, and secure computations have been used for both AI and ML.






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