Hugging Face courses can help you learn natural language processing, transformer models, and how to fine-tune AI models for specific tasks. You can build skills in text generation, sentiment analysis, and deploying machine learning applications. Many courses introduce tools like the Hugging Face Transformers library and datasets from the Hugging Face Hub, that support training and evaluating AI models effectively. You'll also explore practical applications of AI in chatbots, content creation, and language translation.

DeepLearning.AI
Skills you'll gain: Hugging Face, LLM Application, Generative AI, Model Deployment, Cloud Deployment, Natural Language Processing, Large Language Modeling, Applied Machine Learning, User Interface (UI), API Design, Computer Vision
Beginner · Project · Less Than 2 Hours

Skills you'll gain: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Generative Adversarial Networks (GANs), Retrieval-Augmented Generation, Data Collection, Machine Learning Algorithms, Convolutional Neural Networks, Model Deployment, Transfer Learning, Hugging Face
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Hugging Face, Model Evaluation, LLM Application, Large Language Modeling, Model Deployment, Computer Programming, Generative Model Architectures
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Transfer Learning, PyTorch (Machine Learning Library), Model Evaluation, Computer Vision, Retrieval-Augmented Generation, Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Vision Transformer (ViT), Keras (Neural Network Library), LLM Application, Supervised Learning, Vector Databases, Machine Learning, Python Programming, Data Science
Build toward a degree
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Hugging Face, LLM Application, LangChain, Large Language Modeling, OpenAI, Natural Language Processing, Generative AI Agents, ChatGPT, Responsible AI, Embeddings, Application Programming Interface (API), Text Mining, Restful API, Agentic systems, Open Source Technology, Data Preprocessing, MLOps (Machine Learning Operations), Python Programming, Model Evaluation
Beginner · Course · 1 - 4 Weeks

Simplilearn
Skills you'll gain: Hugging Face, Natural Language Processing, Large Language Modeling, Generative AI, Text Mining, Prompt Engineering, AI Workflows, Model Evaluation
Beginner · Course · 1 - 4 Weeks

Duke University
Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Hugging Face, Containerization, CI/CD, DevOps, Docker (Software), Microsoft Azure, Cloud Computing, Machine Learning Software, Transfer Learning, Model Evaluation, GitHub
Advanced · Course · 1 - 4 Weeks

Skills you'll gain: Prompt Engineering, Exploratory Data Analysis, Prompt Patterns, LangChain, Large Language Modeling, Retrieval-Augmented Generation, Model Evaluation, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, LLM Application, Keras (Neural Network Library), Data Transformation, Supervised Learning, Responsible AI, Vector Databases, Data Import/Export
Beginner · Professional Certificate · 3 - 6 Months

Pearson
Skills you'll gain: Generative AI, Large Language Modeling, PyTorch (Machine Learning Library), Generative Model Architectures, Multimodal Prompts, Image Analysis, Model Evaluation, Autoencoders, Hugging Face, Computer Vision, Convolutional Neural Networks, Artificial Neural Networks, LLM Application, Natural Language Processing, Deep Learning, Embeddings, Tensorflow, Transfer Learning, Performance Tuning
Intermediate · Specialization · 1 - 4 Weeks

Duke University
Skills you'll gain: MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis
Advanced · Specialization · 3 - 6 Months

University of California San Diego
Skills you'll gain: Data Structures, Graph Theory, Algorithms, Program Development, Bioinformatics, Data Storage, Development Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Test Case, Programming Principles, Computer Programming, Python Programming, C and C++, Java, Rust (Programming Language), Javascript, Software Testing, Debugging
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Large Language Modeling, Generative AI, Retrieval-Augmented Generation, Generative Model Architectures, PyTorch (Machine Learning Library), Vector Databases, LLM Application, Generative Adversarial Networks (GANs), Embeddings, Natural Language Processing, Hugging Face, Transfer Learning, Data Pipelines, Recurrent Neural Networks (RNNs), Text Mining, Data Ethics, Data Preprocessing, Artificial Intelligence, Performance Tuning
Intermediate · Specialization · 3 - 6 Months
Hugging Face is a leading platform in the field of artificial intelligence, particularly known for its contributions to natural language processing (NLP) and machine learning. It provides a suite of tools and libraries that simplify the development and deployment of machine learning models, making advanced technologies more accessible to developers and researchers. Hugging Face is important because it democratizes AI, allowing individuals and organizations to leverage state-of-the-art models for various applications, from chatbots to content generation. Its open-source approach fosters collaboration and innovation, making it a vital resource in the AI community.‎
Careers related to Hugging Face are diverse and rapidly growing, reflecting the increasing demand for AI expertise. Some potential job roles include machine learning engineer, data scientist, NLP researcher, and AI product manager. These positions often involve working with Hugging Face's tools to build and optimize models, analyze data, and develop AI-driven applications. Additionally, roles in academia and research institutions are available for those interested in pushing the boundaries of AI technology. As businesses continue to integrate AI solutions, the job market for Hugging Face-related positions is expected to expand.‎
To effectively work with Hugging Face(https://www.coursera.org/courses?query=hugging face), several key skills are essential. A strong foundation in programming, particularly in Python, is crucial, as most Hugging Face libraries are built in this language. Understanding machine learning concepts, including supervised and unsupervised learning, is also important. Familiarity with natural language processing techniques, such as tokenization and model training, will enhance your ability to utilize Hugging Face tools. Additionally, knowledge of deep learning frameworks like PyTorch or TensorFlow can be beneficial, as many Hugging Face models are built on these platforms.‎
There are several excellent online courses that focus on Hugging Face and its applications. One notable option is the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate, which provides a comprehensive introduction to machine learning using Hugging Face tools. Another valuable course is Gen AI Using Hugging Face Training, which focuses on generative AI applications. For those interested in MLOps, the MLOps Tools: MLflow and Hugging Face course is a great resource.‎
Yes. You can start learning hugging face on Coursera for free in two ways:
If you want to keep learning, earn a certificate in hugging face, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
Learning Hugging Face can be approached through a combination of structured courses and hands-on practice. Start by enrolling in introductory courses that cover the basics of machine learning and natural language processing. Utilize the resources available on Hugging Face's official website, including documentation and tutorials, to familiarize yourself with their libraries. Engage in practical projects, such as building simple models or contributing to open-source projects, to reinforce your understanding. Joining online communities and forums can also provide support and insights from fellow learners and professionals.‎
Hugging Face(https://www.coursera.org/courses?query=hugging face) courses typically cover a range of topics essential for understanding and utilizing its tools effectively. Key subjects include natural language processing fundamentals, model training and fine-tuning, and the use of pre-trained models for various applications. Courses may also explore advanced topics like transfer learning, deployment strategies, and integration with other machine learning frameworks. Practical exercises and projects are often included to help learners apply their knowledge in real-world scenarios.‎
For training and upskilling employees or the workforce, several Hugging Face courses stand out. The Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate is particularly beneficial for organizations looking to build foundational AI skills among their teams. Additionally, the Gen AI Using Hugging Face Training course can help employees understand generative AI applications, which are increasingly relevant in various industries. These courses provide structured learning paths that can enhance team capabilities in AI and machine learning.‎