Natural Language Processing - Transformers with Hugging Face

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a deep dive into the world of Natural Language Processing (NLP) using Hugging Face's Transformer models. It will equip you with the skills to implement cutting-edge NLP techniques such as sentiment analysis, text generation, named entity recognition, and more. By the end of the course, you will be proficient in applying these models for practical applications in Python. You will start with an introduction to the core concepts behind Transformers, including their evolution from Recurrent Neural Networks (RNNs) to attention mechanisms. The course covers a broad array of topics such as sentiment analysis, embeddings, semantic search, text summarization, and neural machine translation. Each concept is paired with a Python implementation, allowing you to build hands-on experience and gain confidence in real-world NLP applications. Throughout the course, you'll be guided step-by-step through practical examples using the Hugging Face library, which simplifies model training and deployment. By the time you finish, you'll have a solid understanding of various NLP tasks and how to apply Transformers to solve them. You'll also gain insights into advanced topics like masked language modeling, question answering, and zero-shot classification. This course is designed for learners looking to expand their knowledge of NLP, especially those who have a basic understanding of Python and machine learning. If you're eager to get hands-on experience with Hugging Face Transformers and work on real-world applications, this course will be an invaluable resource

• Understand evolution from neural networks to Transformers
• Implement sentiment analysis and text generation models with Hugging Face
• Explore embeddings and semantic search to enhance NLP tasks
• Apply advanced NLP techniques like question answering and zero-shot classification

• Basic understanding of neural networks and NLP concepts
• Readiness for hands-on exercises