This book is a practical guide designed to equip researchers, data scientists, and quality assurance professionals with the knowledge and tools necessary to navigate the machine learning (ML) lifecycle within medicinal products for the human context. The critical stages of model development, training, evaluation, and deployment are explored, providing practical insights and best practices tailored to the medicinal products for the human industry's unique challenges and regulatory requirements. By the end, the reader will feel informed and fully prepared to apply this knowledge in your work. This book emphasizes the importance of computer systems validation in medicinal products for human environments as a foundation of trustworthy and reliable ML systems and the associated training data. It suggests that a validation practice applies to ML. In addition, it highlights the regulatory landscape surrounding ML in medicinal products for humans, outlining the fundamental guidelines and standards that must be adhered to ensure compliance and, most importantly, product quality and patient safety.