Free Audio Course
The Artificial Intelligence Audio Course is a focused, audio-first series that takes you deep into the technical foundations and emerging challenges of modern AI systems. Designed for professionals, students, and certification candidates, this course explains advanced AI concepts through clear, structured narration—no slides, no filler, just direct, practical learning. Each episode unpacks core topics such as neural architectures, model embeddings, optimization, interpretability, and evaluation, showing how these elements come together to create powerful and reliable AI systems. Whether you’re working in development, research, or applied security, the course helps you understand how modern models are designed, trained, and deployed in real-world environments.
Beyond architecture and algorithms, this Audio Course also explores the resilience and trustworthiness of AI—examining attack surfaces, data poisoning, model inversion, and the security controls needed to protect AI systems throughout their lifecycle. It provides insight into ethical risks, bias mitigation, governance frameworks, and assurance practices that keep advanced models safe and compliant. You’ll learn how leading organizations balance innovation with reliability, and how these same principles can guide your own technical and professional growth.
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Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats. As AI moves from research into production, models increasingly influence decisions, automate workflows, and operate in hostile environments where attackers can probe, manipulate, and exploit them. This book frames adversarial machine learning (AML) as a practical security discipline, focused on protecting outcomes, maintaining trust, and ensuring that ML-enabled systems behave reliably when the inputs and operating conditions are not friendly.
The book explores the full lifecycle of AML, providing a structured, real-world understanding of how models can be compromised and what can be done about it. It walks readers through each phase of the machine learning pipeline, showing how weaknesses emerge during data collection and labeling, training and tuning, deployment and integration, and live inference. It breaks adversarial threats into clear categories based on attacker goals, whether to degrade availability, influence or tamper with outputs, steal models, or extract sensitive information from data and predictions. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need, and it explains how small changes in assumptions, interfaces, and observability can turn a “safe” model into an exploitable one.
In addition to diagnosing threats, the book provides a robust overview of defense strategies, from adversarial training and certified defenses to monitoring, privacy-preserving machine learning, and risk-aware system design that treats the model as one component in a larger secure system. Each defensive approach is discussed alongside its limitations and trade-offs, including cost, performance impacts, operational complexity, and where defenses fail under adaptive adversaries. The result is a grounded playbook for engineers, security leaders, and practitioners who need to evaluate real AI risk, choose protections that match the threat model, and build ML systems that remain dependable under pressure.
Adversarial Machine Learning
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