Are you looking to secure your machine learning systems? Look no further than Machine Learning Security Principles – available for a limited time at no cost! This book is an all-inclusive guide to machine learning that explores the processes and security roles that are related to it. In the first section, we will examine these procedures in greater detail to get a better understanding of the security aspects present in machine learning. We will then take a step further and dive into the environments where ML is used, examining the threats and associated coding, diagrams, and real-world scenarios.
Following that, the book moves on to introducing detection techniques for hackers in the modern computing setting and explores how this relates to fraud in ML – from manipulating sales with fake reviews to destruction of adversaries’ reputation. Moreover, the book looks into deep fakes, outlining the potential consequences and how to mitigate them. In addition, the principles of ethical data sourcing in machine learning are discussed, discussing why it’s necessary to remove personal identifiable information (PII) from datasets to reduce the risk of social engineering attacks.
Knowledge Hub Media is a trusted source of machine learning materials, founded in 2020 by Colin Jenkins in San Francisco, California. Colin is an entrepreneur focused on developing and innovating ed-tech solutions, believing in making education a relevant and meaningful experience. To meet his mission of creating an interconnected yet accessible education service, Colin has collaborated with successful tech leaders and developed a deep understanding of modern technology.
By the end of Machine Learning Security Principles, readers will have an elevated level of awareness regarding various attacks and the techniques to secure their machine learning platforms. We hope that this book helps you in doing so! Grab your free copy now before its too late.
