The course aims to consolidate and deepen the skills acquired in the Data Science in Healthcare course, focusing on advanced methodologies and their operational application in healthcare contexts. The program involves an in-depth study of advanced Machine Learning and Deep Learning techniques, particularly effective in processing complex data, such as images, text, and graphs. Starting with an introduction to neural networks and the foundational concepts necessary to understand how they work, the course outlines the main developments in the fields of Deep Learning and Machine Learning. It aims to provide the theoretical foundations that guide these advanced techniques, their most significant applications, and the most employed training strategies. Furthermore, the course addresses methodologies and technologies for the operation and management of data mining (DataOps) and machine learning (MLOps) pipelines within business, hospital, and cloud contexts. By the end of the course, students will have gained an in-depth understanding of the fundamental principles of Machine Learning and Deep Learning techniques learned, not only being able to implement them but also to critically evaluate their performance and identify potential limitations. The course will also provide the tools to identify and mitigate the main challenges associated with these technologies, both in the design phase and in practical application. Students will thus be capable of developing complex machine learning pipelines to tackle and solve specific problems in the healthcare domain, from design to practical implementation, up to deployment and operational management in production environments. They will also have acquired the skills to apply deep learning techniques to real-world problems in the healthcare domain, leveraging a critical approach and awareness of the challenges and opportunities presented by these advanced technologies.