Learn Pretrained Models

Discover, use, and fine-tune state-of-the-art AI models for computer vision, natural language processing, audio, and multi-modal tasks. From Hugging Face Hub to deployment in production.

Start Course → View All Lessons
9
Lessons
80+
Models Covered
~4hr
Total Time
100%
Free

What You'll Learn

A comprehensive guide to the world of pretrained AI models.

🤖

Model Discovery

Navigate Hugging Face Hub, find the right model for your task, and understand model cards and benchmarks.

👁

Vision, Language & Audio

Explore models for image classification, text generation, speech recognition, and everything in between.

🛠

Using Models

Load pretrained models, run inference, batch process data, and serve models via APIs.

🎯

Fine-tuning

Adapt pretrained models to your data with LoRA, QLoRA, and the Hugging Face Trainer API.

Course Lessons

Follow the lessons in order or jump to any topic you need.

Beginner

1. Introduction

What are pretrained models? Transfer learning, model types, where to find models, and common formats.

10 min read →
 Beginner

2. Hugging Face Hub

Browse, download, and use models from the largest model hub. Pipeline API and AutoModel classes.

15 min read →
 Intermediate

3. Vision Models

ResNet, ViT, YOLO, SAM, Stable Diffusion — pretrained models for every computer vision task.

15 min read →
 Intermediate

4. Language Models

GPT-2, Llama, BERT, T5, sentence-transformers — models for generation, classification, and embeddings.

15 min read →
 Intermediate

5. Audio Models

Whisper, Bark, MusicGen — models for speech recognition, text-to-speech, and audio generation.

12 min read →
 Intermediate

6. Multi-Modal Models

CLIP, LLaVA, Stable Diffusion, LayoutLM — models that work across text, image, video, and documents.

12 min read →
 Intermediate

7. Using Models

Step-by-step guide to loading, inference, batch processing, GPU acceleration, and serving models.

15 min read →
 Advanced

8. Fine-tuning

Adapt models to your data with LoRA, QLoRA, Trainer API, and efficient fine-tuning techniques.

15 min read →
 Advanced

9. Best Practices

Model selection, licensing, ethical use, deployment strategies, and common mistakes to avoid.

10 min read →

Prerequisites

What you need before starting this course.

Before You Begin:
  • Python programming experience
  • Basic understanding of machine learning concepts
  • Familiarity with pip or conda for package management
  • A GPU is helpful but not required (Google Colab provides free GPUs)