Overview

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This course features Coursera Coach! 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.

Syllabus

Course Introduction

In this module, we will introduce the course structure and objectives. You will understand what to expect and how to navigate the learning journey with Hugging Face. This section sets the stage for practical, hands-on exploration of AI models and tools.

Getting Started with Hugging Face

In this module, we will explore Hugging Face’s ecosystem. You will learn its core features, interface, and how to prepare your development environment. This foundation ensures smooth progression into model and dataset handling.

Exploring Models

In this module, we will dive into Hugging Face models. You will understand model details, leverage SDK tools, and manage authentication for seamless workflow integration.

Understanding Transformers Library

In this module, we will cover the Transformers library fundamentals. You will learn how to configure, customize, and deploy models for various NLP and AI tasks.

Exploring the Datasets Library

In this module, we will explore the Hugging Face datasets library. You will handle data preparation, optimize memory usage, and contribute custom datasets to the Hub.

Fine-Tuning and Training with Hugging Face

In this module, we will focus on fine-tuning and training workflows. You will learn to optimize models, track performance, and prepare models for real-world applications.

Optimization and Scaling with Hugging Face

In this module, we will teach strategies to scale and optimize models. You will improve training efficiency and leverage hardware resources effectively for large-scale AI tasks.

Model Deployment Using Hugging Face Spaces

In this module, we will guide you through model deployment. You will create interactive applications and share your AI models seamlessly with Hugging Face Spaces.

Conclusion

In this module, we will wrap up the course with key insights. You will reflect on learned skills and explore ways to continue advancing your Hugging Face expertise.