Master the Hugging Face ecosystem—the leading open-source platform for machine learning. This hands-on course teaches you to discover, evaluate, and deploy pre-trained models for text, image, and audio tasks without training from scratch.
You'll learn to navigate the Hugging Face Hub to find models among 500,000+ options, read model cards to make informed selections, and understand licensing for commercial use. Through practical exercises, you'll build inference pipelines using the Transformers library, process datasets efficiently with streaming for large-scale data, and deploy models across different hardware (NVIDIA GPUs, Apple Silicon, CPU).
The course culminates in building a multi-modal content analyzer that classifies text sentiment, categorizes images, transcribes audio, and generates captions—demonstrating how modern ML practitioners leverage pre-trained models to solve real problems quickly.
Designed for developers and data scientists who want to accelerate their ML workflows, this course provides the foundation for fine-tuning and deploying Hugging Face models in production environments. All exercises use real-world scenarios from healthcare, fintech, and media industries.
This module introduces the Hugging Face ecosystem and teaches you to find, evaluate, and select models for your projects. You'll learn to navigate the Hub's search and filtering system, understand model file structures, evaluate licensing for your use case, and configure authentication for programmatic access.
涵盖的内容
5个视频3篇阅读材料1个作业
显示有关单元内容的信息
5个视频•总计17分钟
What Is Hugging Face•4分钟
Searching and Filtering Models•4分钟
Your Account and Organizations•4分钟
Model Files•2分钟
Licensing and Usage Rights•4分钟
3篇阅读材料•总计3分钟
Getting Started•1分钟
About this course and your instructors•1分钟
Reflection•1分钟
1个作业•总计5分钟
Quiz: Getting Started•5分钟
Working with Datasets
第 2 单元•小时 后完成
单元详情
This module covers discovering and loading datasets from the Hugging Face Hub. You'll learn to explore the datasets hub, load data programmatically with Python, and use streaming for datasets too large to download.
涵盖的内容
3个视频3篇阅读材料1个作业
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3个视频•总计11分钟
Exploring Datasets Hub•3分钟
Loading Datasets With Python•4分钟
Streaming Large Datasets•4分钟
3篇阅读材料•总计30分钟
Key Concepts: Work with Datasets•10分钟
Working With Data Lab•10分钟
Reflection•10分钟
1个作业•总计5分钟
Quiz: Working with Datasets•5分钟
Putting It Together
第 3 单元•25分钟 后完成
单元详情
Practice the core concepts of the Hugging Face Ecosystem
涵盖的内容
2篇阅读材料1个作业
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2篇阅读材料•总计20分钟
Exploring Hugging Face with Colab Notebooks•10分钟
Inference Loop Lab•10分钟
1个作业•总计5分钟
Coding with Hugging Face•5分钟
Capstone Project and Critical Thinking Assessment
第 4 单元•小时 后完成
单元详情
This capstone project demonstrates mastery of the Hugging Face Hub and ecosystem by building a multi-modal content analysis system. You'll discover and compare models across modalities, explore datasets, build inference pipelines, and create a unified analysis workflow.
涵盖的内容
2篇阅读材料1个作业
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2篇阅读材料•总计20分钟
Final Key Concepts Review•10分钟
Capstone Project•10分钟
1个作业•总计15分钟
Final Graded Quiz•15分钟
Next Steps
第 5 单元•10分钟 后完成
单元详情
Learn About New Opportunities To Test Your Knowledge
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.