This tutorial is a comprehensive, end-to-end guide to the Hugging Face ecosystem, showing how modern AI moves from research ideas to real, deployed systems.

Rather than focusing on a single model or task, the course presents Hugging Face as the operating system of modern AI—connecting models, datasets, libraries, demos, and deployment into one coherent, practical workflow.

Source Code (GitHub): https://github.com/MOHAMMEDFAHD/Hugging-Face/tree/main/Hugging-Face-Course

Hugging Face Atlas Page: https://programming-ocean.com/knowledge-hub/hugging-face-atlas.php

Course developed by @programmingoceanacademy 

⭐️ Chapters ⭐️
— 0:00:00 Introduction
— 0:01:22 Transformers Library in Hugging Face
— 2:07:45 Audio Models in Hugging Face
— 2:59:08 Diffusers Library in Hugging Face
— 3:56:03 Video Models in Hugging Face
— 4:35:37 Gradio Library in Hugging Face
— 6:18:37 Spaces and Deployment in Hugging Face

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A hands-on course covering the Hugging Face ecosystem end-to-end, starting with loading and using GPT-2 via the Transformers library. Covers tokenization in depth (subword tokenization, token IDs, encoding/decoding), then walks through text generation using both the high-level pipeline API and the lower-level AutoTokenizer/AutoModelForCausalLM approach. Explains and implements four token sampling strategies from scratch: greedy decoding, top-K sampling, top-P (nucleus) sampling, and temperature sampling, with live demonstrations in Google Colab.

Deploying AI Models with Hugging Face – Hands-On Course