Practical AI

This book provides a comprehensive yet accessible introduction to the foundations, applications, and limitations of Artificial Intelligence, with a strong emphasis on practical relevance across industries. Designed for students without a technical background, the book explains the principles of data-driven models, classical machine learning, and modern generative AI, while addressing ethical, legal, and societal questions. The book walks through the full lifecycle of an AI product: from the foundational differences between AI, machine learning, and deep learning, through data quality and governance, model validation, and the operational realities of moving a prototype into production.

Beyond the technical groundwork, Practical AI also tackles the questions that determine whether an AI initiative survives contact with the real world: how to structure a project using a dedicated AI Project Canvas, which roles and competencies a team actually needs, how to weigh cloud versus on-premises infrastructure and estimate real costs, and how to navigate an increasingly complex regulatory landscape, including the EU AI Act and region-specific data protection rules. A dedicated chapter on generative AI and large language models brings the book fully up to date, covering prompting principles, AI-assisted coding, and the opportunities and risks of working with these tools. Every chapter closes with exercises and worked solutions, and a capstone project chapter guides readers through producing a final report and pitch: making this as much a hands-on course companion as a reference for self-study.

Whether used as a semester-long textbook or read cover to cover by a working professional, Practical AI offers a clear, no-code roadmap for anyone who needs to plan, manage, or evaluate an AI project with confidence.