Natural Language Processing with Attention Models

DeepLearning.AI organizes attention-based NLP around three applications: translation, summarization and question answering. The provider recommends the first three courses of its NLP sequence. This makes the course a continuation for learners ready to connect attention mechanisms with complete language tasks.

What you will learn

• Apply attention-based methods to a translation task.
• Explain the modeling choices in text summarization.
• Develop and evaluate a question-answering workflow.

Who this suits

Intermediate learners with preparation equivalent to the first three courses in DeepLearning.AI’s NLP specialization.

Before you enroll

Check the expected deep-learning framework and prior sequence-model knowledge. Evaluate factual consistency in summaries and answers; fluent wording alone does not establish correctness.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Test a summarization model on a short factual passage. Check every claim against the input and record omitted details or unsupported additions.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.