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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course empowers you to effectively build, fine-tune, and deploy AI models using Hugging Face. You'll gain hands-on experience with models, datasets, the Transformers library, and deployment interfaces, equipping you with skills to create real-world AI applications. Throughout the course, you will start by exploring the Hugging Face ecosystem, learning about model and dataset cards, and setting up your development environment. You’ll progress to using the Hugging Face Hub, working with the Python SDK, and understanding authentication and access management. Next, the course guides you through the Transformers and Datasets libraries, covering architecture, tokenization, dataset management, and pipeline customization. You will then dive into fine-tuning, training, evaluation, and optimization techniques using Accelerate, gradient checkpointing, and the Optimum library. Finally, you’ll learn to deploy models using Hugging Face Spaces, leveraging Gradio and Streamlit interfaces. This course is ideal for developers, data scientists, and AI enthusiasts with basic Python knowledge who want a practical, hands-on approach to building scalable AI solutions. Difficulty level: Intermediate. By the end of the course, you will be able to efficiently explore Hugging Face models and datasets, fine-tune and optimize AI models, implement custom pipelines, and deploy fully functional AI applications.
