Machine learning is reshaping industries at breakneck speed, but most explanations are locked behind a wall of math and code. This book tears down that wall. Written expressly for the non-technical professional, it provides a clear, honest, and actionable understanding of how machine learning works, what it can and cannot do, and how to use it for real business advantage. You'll start with the core ideas: how algorithms learn from examples, why data quality matters, and where bias and hallucination creep in. Then you'll move to practical application: templates for vetting data readiness, checklists for evaluating vendors, and a simple framework for running your first ML experiment. The final sections focus on profit—identifying high-impact use cases like predictive maintenance, customer segmentation, and dynamic pricing, while avoiding the traps that waste time and money. Real-world examples from retail, healthcare, and finance illustrate how organizations of all sizes have turned ML into profit, along with the common pitfalls they encountered. You'll also get clear guidance on building an experimentation culture, setting realistic expectations, and measuring ROI. By the end, you'll be equipped to lead data-driven conversations, ask incisive questions, and seize opportunities that others miss. No PhD or prior experience needed—just curiosity. Part of The Practical AI Library.
About the author
Marcus Chen Ferro writes for The Practical AI Library.
