Practical AI
A Blueprint for Building Intelligent Products
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Covers the full AI project lifecycle for students and professionals without programming or advanced math backgrounds Tailored for business, management, and applied sciences students, showing sector-specific applications: no coding needed Includes exercises, solutions, an AI Project Canvas, and a capstone project for hands-on classroom or self-study use
Autor*in
Umberto Michelucci
Themen in »Practical AI«
AI project management Machine learning for business MLOps and AI deployment AI product development Generative AI AI governance and regulation Ethical and Responsible AI Technology Lifecycle Data Quality and Fairness AI Applications in Business and Society