This comprehensive course on Generative AI using Hugging Face equips you with the skills to build real-world NLP applications powered by transformer models. Begin with an introduction to the Hugging Face ecosystem and its role in accelerating AI development. Gain hands-on experience with speech-to-text pipelines and learn how to convert audio into accurate transcripts using pre-trained models. Progress to building sentiment analysis tools that interpret user feedback and classify emotions from text data. Advance your skills in natural language generation by leveraging Hugging Face's pre-trained transformers to create human-like content at scale, ideal for chatbots, summaries, and automated writing.


您将学到什么
Understand LLM capabilities like summarization and content translation
Build real-world applications using chatbots, virtual assistants, and sentiment analysis
Evaluate LLM performance using ROUGE, GLUE, SuperGLUE, and BIG-bench benchmarks
Analyze industry use cases and deployment strategies for LLM-powered solutions
您将获得的技能
要了解的详细信息

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July 2025
3 项作业
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该课程共有1个模块
Explore the power of Generative AI using Hugging Face in this hands-on module. Begin with an introduction to Hugging Face and its ecosystem. Learn to implement speech-to-text applications and build sentiment analysis models. Progress to advanced text generation using pre-trained transformers for real-world use cases in NLP and AI content creation.
涵盖的内容
7个视频1篇阅读材料3个作业
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常见问题
Yes, many e-learning platforms like Coursera, Simplilearn, Udemy offers free and paid learning resources through their Hugging Face Course, covering transformers, NLP, and model deployment. It's designed for beginners and advanced users alike.
The best Generative AI course depends on your goals. For practical NLP applications, Hugging Face’s course is highly rated. For broader GenAI skills, programs by Coursera, Simplilearn, and DeepLearning.AI offer industry-recognized content.
Yes, the Hugging Face Course is completely free and open-source. It includes hands-on tutorials and is regularly updated to cover the latest in transformers, datasets, and NLP techniques.
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