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.

Umberto Michelucci has a PhD in Machine Learning and Physics  and is currently a professor of scientific machine learning at the Lucerne University of Applied Sciences and founder of the Applied AI Center at the university. He is the cofounder and Chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. Dr. Michelucci is an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. He has published four books with Springer and Apress. He’s very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences.