Natural Language Processing with Sequence Models

This course moves from recurrent language models into LSTMs and entity recognition, then studies Siamese networks. DeepLearning.AI recommends completing the earlier two NLP courses first. It is a focused continuation for learners who want to model ordered language data rather than treat words independently.

What you will learn

• Explain a recurrent network’s role in language modeling.
• Apply LSTM-based methods to an entity-recognition task.
• Compare paired inputs using a Siamese-network approach.

Who this suits

Learners who have completed, or can match the skills from, the first two courses in DeepLearning.AI’s NLP specialization.

Before you enroll

Preview the required coding framework and sequence-model background. Check how padding, sequence length and evaluation splits affect results before changing a network architecture.

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Put it into practice

Compare entity predictions on a short sentence and a longer version with the same key names. Investigate whether sequence length changes the errors.

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.

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