Machine Learning Security Principles
As hackers come up with new ways to mangle or misdirect data in nearly undetectable ways to obtain access, skew calculations, and modify outcomes. Machine Learning Security Principles helps you understand hacker motivations and techniques in an easy-to-understand way.
Offered by
Difficulty Level
Intermediate
Completion Time
15h
Language
English
About Book
Who Is This Book For?
Whether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.
Machine Learning Security Principles
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h total length
Defining Machine Learning Security
Mitigating Risk at Training by Validating and Maintaining Datasets
Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
Considering the Threat Environment
Keeping Your Network Clean
Detecting and Analyzing Anomalies
Dealing with Malware
Locating Potential Fraud
Defending against Hackers
Considering the Ramifications of Deepfakes
Leveraging Machine Learning against Hacking
Embracing and Incorporating Ethical Behavior
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