Pearson
Introduction to Transformer Models for NLP: Unit 2

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Pearson

Introduction to Transformer Models for NLP: Unit 2

Pearson

Instructor: Pearson

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

August 2025

Assessments

6 assignments

Taught in English

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This course is part of the Introduction to Transformer Models for NLP Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There is 1 module in this course

This module provides a comprehensive exploration of modern transformer-based models for natural language processing. It covers the foundational architectures and mechanisms of BERT and GPT, delving into their pre-training, fine-tuning, and practical applications. Through hands-on lessons, learners engage with real-world tasks such as sequence and token classification, question answering, semantic search, and text generation. The module emphasizes both theoretical understanding and practical skills, enabling students to leverage BERT and GPT for a wide range of NLP challenges, including multi-task learning and adapting models to new domains or writing styles.

What's included

24 videos6 assignments

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Pearson
Pearson
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