This toolkit is intended to provide you a framework that addresses privacy and governance at both the organizational and system levels. It provides suggestions for determining the right stakeholders to engage

The first focus of this course is on the application, practice and development opportunities of AI in the healthcare industry, with a particular focus on the needs and realities of developing countries. It covers core AI technologies and their healthcare applications, practical paths for implementing AI healthcare projects in developing countries, and global trends and regional opportunities. Unique benefits include case studies tailored to developing regions, actionable implementation strategies, and insights into leveraging global resources for local development.

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

This learning path guides you through a curated collection of content on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud. - Define Generative AI; - Explain how Generative AI works; - Describe Generative AI Model Types; and - Describe Generative AI Applications.