This course provides practical instruction on transformer architectures such as BERT, GPT, and T5. You will learn about attention mechanisms, transfer learning, and model fine-tuning through coding exercises and case studies. By the end, you will be able to build and optimize NLP models for various applications.
Introduction to Transformer Models for NLP Specialization
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Introduction to Transformer Models for NLP Specialization
LLMs, Transformers, and Mechanisms for NLP Models. Use top transformer LLMs like BERT, GPT-3, ChatGPT and T5 to tackle modern NLP challenges.
Instructor: Pearson
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Get in-depth knowledge of a subject
Intermediate level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Intermediate level
Recommended experience
1 month at 5 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Master the core concepts and mathematics behind transformer architectures, including attention mechanisms and embeddings.
Gain experience fine-tuning and deploying advanced models BERT, GPT, and T5 for tasks like classification, question answering, and text generation.
Learn to apply transfer learning, MLOps, and deployment strategies to create and share robust NLP solutions in real-world scenarios.
Overview
Skills you'll gain
What鈥檚 included
Shareable certificate
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Taught in English
Recently updated!
September 2025
14 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Pearson
Specialization - 3 course series
What you'll learn
Understand the evolution of NLP architectures and the transformative impact of attention mechanisms.
Analyze the structure and mathematical foundations of transformer models, including scaled dot product and multi-headed attention.
Apply transfer learning techniques using pre-trained language models such as BERT and GPT.
Gain practical experience with PyTorch to fine-tune NLP models for custom tasks.
Skills you'll gain
Category: PyTorch (Machine Learning Library)
Category: Natural Language Processing
Category: Large Language Modeling
Category: Generative AI
Category: Artificial Neural Networks
Category: Deep Learning
What you'll learn
Master the architectures and core mechanisms of BERT and GPT for natural language understanding and generation.
Fine-tune pre-trained models for advanced NLP tasks such as classification, question answering, and semantic search.
Apply hands-on techniques to customize BERT and GPT for specific domains and writing styles.
Utilize prompt engineering and few-shot learning to solve multiple NLP tasks efficiently.
Skills you'll gain
Category: Natural Language Processing
Category: Generative Model Architectures
Category: Large Language Modeling
Category: Prompt Engineering
Category: Text Mining
Category: Semantic Web
What you'll learn
Understand and apply the T5 model鈥檚 end-to-end transformer architecture for advanced NLP tasks.
Fine-tune and evaluate transformer models for complex applications like abstractive summarization.
Leverage Vision Transformers and build custom image captioning systems by combining vision and language models.
Deploy, share, and operationalize transformer models using modern MLOps tools and cloud frameworks.
Skills you'll gain
Category: MLOps (Machine Learning Operations)
Category: Natural Language Processing
Category: Computer Vision
Category: Deep Learning
Category: Large Language Modeling
Category: Cloud Hosting
Category: Restful API
Category: Image Analysis
Category: Application Deployment
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Frequently asked questions
This course is completely online, so there鈥檚 no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don鈥檛 give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you鈥檙e automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
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