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Companies are harnessing the power of AI to make previously complex and expensive operations much easier, faster and cheaper.
The first part of this document describes these processes in more detail. This helps us understand the role security plays in machine learning.
As we progress to the second part, we’ll learn more about the environments in which ML is commonly used, and dive deeper into the security threats plaguing ML with code, graphics, and real-world references.
The next part of this book describes the process of detecting hacker activity in modern computing environments. Fraud can take many forms in ML, from generating sales with fake reviews to destroying the reputation of adversaries. After understanding hackers’ goals and detection techniques, learn about the impact of deep fakes and mitigation strategies.
The book also describes best practices for employing ethical data sourcing that mitigate the security risks associated with your data. See how the simple act of removing personally identifiable information (PII) from your dataset reduces the risk of social engineering attacks.
By the time you finish reading this machine learning book, you’ll have a better understanding of various attacks and techniques for effectively defending against ML systems.
This free offer expires May 17th.
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Machine Learning Security Principles (worth $37.99) – Free Download
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