Natural Language Processing in TensorFlow

DeepLearning.AI takes TensorFlow from text sentiment into embeddings and sequence models, ending with a literature-related sequence module. The provider recommends the first two courses in its TensorFlow specialization. It is a framework-focused route for learners who already have basic neural-network coding experience.

What you will learn

• Prepare a TensorFlow model for a sentiment task.
• Use word embeddings as a text representation.
• Apply sequence models to language data.

Who this suits

Learners comfortable with Python and high-school mathematics, with preparation equivalent to the first two TensorFlow specialization courses.

Before you enroll

Verify TensorFlow and Keras versions in the notebooks. Tokenization APIs and sequence utilities can change, so check current documentation when an older example fails.

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Put it into practice

Train a small sentiment model and compare familiar phrases with examples containing negation. Inspect whether the representation captures the difference.

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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