Free Audio Course
The Introduction to Artificial Intelligence Audio Course is your complete, audio-first guide to understanding the principles, possibilities, and real-world impact of AI. Designed for learners at any stage—students, professionals, or career changers—this Audio Course takes you on a structured journey through how machines learn, reason, and make decisions. Each episode builds your understanding step by step, covering the fundamentals of machine learning, neural networks, natural language processing, robotics, and data-driven intelligence. You’ll also explore how AI is transforming industries such as healthcare, finance, cybersecurity, and transportation, gaining both conceptual clarity and practical awareness along the way.
Artificial Intelligence (AI) represents the next major evolution in computing, where systems are designed to perform tasks that traditionally require human intelligence. From pattern recognition and prediction to autonomous action and adaptive learning, AI technologies are redefining how people and organizations solve problems. This course also examines the ethical, regulatory, and societal implications of AI—exploring topics like algorithmic bias, transparency, and the future of human-machine collaboration. By the end of the series, you’ll not only understand the technical foundations but also the strategic and ethical dimensions shaping the future of AI innovation.
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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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