The Global Facilitator Training is an innovative hybrid programme designed to empower facilitators and trainers to operate effectively in international and multicultural contexts. The training begins with a creative Sprint (Day 1) - a collaborative design process where participants co-create the Global Facilitator Profile, defining the skills, values, and mindset that characterize global excellence in facilitation. Building on the outcomes of this creative phase, participants advance into the Facilitator Certification Training (Day 2,3,4) an advanced learning pathway that integrates high-level facilitation skills with a strong emphasis on social justice, development cooperation principles, and emotional intelligence, coaching models, intercultural facilitation and inclusion.

An online course focused on building technology which: - Respects Human Nature - Minimizes Harmful Consequences - Centers Values - Creates Shared Understanding - Supports Fairness and Justice - Helps People Thrive A graduate of this course is prepared to bring a sophisticated perspective on humane technology to the product development process.

Big data and data analytics emerged quickly in the past decade and they aren't buzz words; they are a reality and they are all making an impact in education. Learning analytics are becoming essential for data-driven decisions in education. A key component of the future of learning institution, as they are used to optimize learning environments for students and employees. The data generated by digital learning ecosystem can guide training managers in making informed data-driven decisions, and students seeking for a personalized learning experience. By taking this course you will learn how to make data-driven decisions to optimize learning and the environments in which it occurs. The course is designed to increase your understanding of learning analytics and it will prepare you to make data-driven decisions about education improvement using a broad range of data collection, analytical, and visualization methods.

By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners