This course immerses students in the world of generative artificial intelligence (GenAI) and covers high-level browser-based tools, emphasizing the Python code behind them.
Demonstrate a comprehensive understanding of key Generative AI concepts, including tokenization, vectorizing text, and attention mechanisms
Apply Generative AI techniques to create AI-generated content, such as images and videos, from text prompts
Optimize and fine-tune AI models on custom datasets and problems to enhance and extend the GenAI models
About this course:
This course equips students with the knowledge and skills to work with cutting- edge generative AI models. The course blends theoretical foundations with hands-on implementation to prepare students for real-world challenges in creative industries, healthcare, education, and more. Students will explore topics such as diffusion models, large language models (LLMs), multimodal AI, and prompt engineering, learning to guide and optimize generative outputs. Practical sessions using Python, PyTorch, and Hugging Face provide hands-on experience in deploying generative AI applications. By the end of the course, students will develop a capstone project, demonstrating their expertise and creativity in generative AI.
Prerequisites
Before embarking on the Generative AI with Applications in Python course, it is imperative to establish a robust foundational knowledge. We suggest the following preparatory courses: Introduction to Data Science to acquaint yourself with the basic principles of data science and Machine Learning Using Python.
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