beginnerFree

Open Source Models with Hugging Face

Name: Open Source Models with Hugging Face
Rating: 4.5 (3800 reviews)

by Marc Sun & Younes Belkada · DeepLearning.AI

4.5

(3,800 reviews)

65K+ enrolled1 hourUpdated 2024-07

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Our Verdict

Worth it — with caveats

Open Source Models with Hugging Face is the fastest credible way for a Python-literate beginner to start running open-source models hands-on, but it is a one-evening primer, not a real course. This free DeepLearning.AI short course, built with Hugging Face and taught by three of its machine learning engineers (Marc Sun, Younes Belkada, and Maria Khalusova), is a roughly one-hour, code-along tour of the transformers library across 13 short lessons spanning text, audio, image, and multimodal tasks, ending with deploying a demo via Gradio and Hugging Face Spaces. Its standout value is that you run real models locally with no paid API calls, making it a low-friction first step into the Hugging Face ecosystem. Learner sentiment is strongly positive: the Coursera Guided Project version holds 4.8/5 from 155 reviews, with reviewers praising it as a straightforward, hands-on introduction. The trade-off is breadth over depth: it demonstrates many pipelines at a surface level and does not teach fine-tuning, model internals, or production engineering.

Take it if you are a beginner or developer who wants a quick, practical orientation to running open-source models with the Hugging Face transformers library at zero API cost. Treat it as a one-evening primer, not a comprehensive course: at about an hour it is intentionally shallow, skips fine-tuning and theory, and offers no certificate on the free DeepLearning.AI platform.

Best for: Beginners and working developers who already know basic Python and want a fast, hands-on overview of the Hugging Face transformers library and the Hub. It suits people who want to see how to run NLP, audio, image, and multimodal models locally (no paid API), and who want a quick path to demoing an app via Gradio and Hugging Face Spaces.

Skip if: People who want depth: anyone seeking to fine-tune or train models, understand transformer internals or the math, or learn production-grade MLOps. It is also a poor fit for complete non-programmers (it assumes Python) and for learners who specifically need a shareable certificate from the free version.

About This Course

Use open-source models from Hugging Face Hub for NLP, audio, image, and multimodal tasks without API costs.

What You'll Learn

Find and filter open-source models on the Hugging Face Hub based on task and requirements

Use the transformers library pipeline API for NLP tasks: chat/text generation, translation, summarization, and sentence embeddings for search and similarity

Work with audio models for zero-shot audio classification, automatic speech recognition (ASR), and text-to-speech (TTS)

Apply computer vision models for object detection, image segmentation, image retrieval, image captioning, and zero-shot image classification

Use multimodal models for visual question answering and image search/captioning

Run these open-source models locally without incurring per-call API costs

Build and share a simple AI app demo using Gradio and Hugging Face Spaces

Curriculum

Introduction

Course overview and orientation to using open-source models from the Hugging Face Hub.

NLP

Use the transformers library to build a chatbot from a small language model for multi-turn conversation.

Translation and Summarization

Translate between languages and summarize documents with open-source text models.

Sentence Embeddings

Generate embeddings to measure text similarity for search and retrieval use cases.

Zero-Shot Audio Classification

Classify audio clips into categories without task-specific labeled training data.

Automatic Speech Recognition

Convert audio speech to text using ASR models.

Text to Speech

Convert text into spoken audio using TTS models.

Object Detection

Detect and locate objects within images.

Segmentation

Perform pixel-level image segmentation, including zero-shot segmentation.

Image Retrieval

Find images that are similar or relevant using vision/multimodal models.

Image Captioning

Generate natural-language descriptions for images.

Visual Q&A

Answer questions about the content of an image with multimodal models.

Zero-Shot Image Classification

Classify images into arbitrary categories without labeled training examples.

Deployment

Demo and share your AI app locally, on the cloud, or via API using Gradio and Hugging Face Spaces.

Prerequisites

Basic Python programming
Comfort running code in Jupyter notebooks
Helpful but optional: basic familiarity with machine learning / AI concepts
No GPU or paid API key required to follow along

Instructor

Marc Sun & Younes Belkada

Instructor · DeepLearning.AI

Pros & Cons

Pros

Free on the DeepLearning.AI learning platform and taught directly by Hugging Face ML engineers, so the guidance reflects current, idiomatic library usage
Highly practical and code-along: each short lesson maps to a runnable transformers pipeline you can reproduce
Genuinely broad task coverage in one hour: text, audio, image, and multimodal, ending in a deployable Gradio/Spaces demo
Runs open-source models locally with no paid API key required, lowering the barrier to experimenting
Strong, consistent learner reception (4.8/5 on Coursera) with reviewers calling it a clear, well-organized introduction

Cons

Very shallow by design: about one hour total means each topic is a quick demo, not deep understanding
Does not cover fine-tuning, training, evaluation, or model internals/theory
No production/MLOps depth beyond a basic Gradio + Hugging Face Spaces demo
Certificate caveat: the free DeepLearning.AI version has no certificate, and the Coursera Project version is paid with no audit option

Alternatives To Consider

NLP Course

Hugging Face

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LangChain for LLM Application Development

DeepLearning.AI

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Practical Deep Learning for Coders

fast.ai

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Frequently Asked Questions

Is Open Source Models with Hugging Face free?

Yes — Open Source Models with Hugging Face is free to access. Free to take on the DeepLearning.AI learning platform during its beta (no certificate). The same content is also offered as a paid Coursera Guided Project, where auditing is not available; a certificate is included only with the paid Coursera version. The models used are open-source, so following along incurs no API/inference fees.

Who is Open Source Models with Hugging Face for?

Beginners and working developers who already know basic Python and want a fast, hands-on overview of the Hugging Face transformers library and the Hub. It suits people who want to see how to run NLP, audio, image, and multimodal models locally (no paid API), and who want a quick path to demoing an app via Gradio and Hugging Face Spaces.

What will you learn in Open Source Models with Hugging Face?

Find and filter open-source models on the Hugging Face Hub based on task and requirements; Use the transformers library pipeline API for NLP tasks: chat/text generation, translation, summarization, and sentence embeddings for search and similarity; Work with audio models for zero-shot audio classification, automatic speech recognition (ASR), and text-to-speech (TTS); Apply computer vision models for object detection, image segmentation, image retrieval, image captioning, and zero-shot image classification.

What are the prerequisites for Open Source Models with Hugging Face?

Basic Python programming; Comfort running code in Jupyter notebooks; Helpful but optional: basic familiarity with machine learning / AI concepts; No GPU or paid API key required to follow along.

Is Open Source Models with Hugging Face worth it?

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on DeepLearning.AI's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free

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