This course offers an introductory exploration of Generative Artificial Intelligence (GAI), a transformative field focused on creating AI systems capable of generating new and realistic data. Participants will delve into the foundational concepts of generative AI, including key principles and techniques used in the generation of diverse and creative outputs. The course covers prominent generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), providing insights into their architectures and functionalities. Applications of generative AI across various domains, from image and text generation to data augmentation, will also be discussed. Ethical considerations and challenges in the development and deployment of generative AI systems are addressed, emphasizing responsible AI practices. Additionally, the course explores future trends and opportunities in the field, along with practical guidance on building and deploying generative AI applications.

Who This Course Is For:

This course is designed for students, researchers, developers, and professionals seeking an introductory understanding of Generative Artificial Intelligence (GAI). Whether you are a data scientist exploring innovative AI techniques, a developer interested in integrating generative AI into your projects, or an enthusiast curious about the capabilities of GAI technology, this course provides a foundational overview suitable for individuals with diverse backgrounds in AI, computer science, engineering, and related fields. No prior experience with generative AI is required, making it accessible to learners at various stages of their AI journey.