Generative AI (GenAI)

Course Introduction

Generative AI is a revolutionary field of artificial intelligence that enables machines to generate new content, such as text, images, and music, mimicking human creativity. This course provides a comprehensive introduction to Generative AI, covering foundational concepts, various generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), and their applications in real-world scenarios. By the end of this course, you will gain hands-on experience in building and fine-tuning your own generative models, preparing you for advanced research or a career in AI-driven innovation.

Course Description

This 10-week course is designed to provide an in-depth understanding of Generative AI technologies. It will cover the fundamentals of machine learning and deep learning, focusing on how generative models are built and utilized across different domains. The course will explore various types of generative models, including autoregressive models, GANs, VAEs, and transformers, with practical coding exercises and projects to reinforce learning. Participants will learn how to design, train, and evaluate generative models, ensuring they understand the ethical implications and challenges associated with their deployment.

Certification

Upon successful completion of the course, participants will receive a “Certificate in Generative AI Fundamentals.” This certification will affirm your knowledge and skills in Generative AI, showcasing your ability to develop and apply generative models. It will be a valuable addition to your professional portfolio, especially for roles in AI development, data science, and digital content creation.

Learning Outcomes

By the end of this course, participants will be able to:

1. Understand the fundamental principles of Generative AI and its various applications.
2. Explain the workings of different generative models such as GANs, VAEs, and autoregressive models.
3. Develop and implement basic generative models using Python and popular AI frameworks like TensorFlow and PyTorch.
4. Evaluate the performance of generative models and fine-tune them for specific applications.
5. Discuss the ethical considerations and potential impacts of deploying generative AI systems in society