Natural Language Processing with Classification and Vector Spaces

DeepLearning.AI starts language processing with two sentiment classifiers, then moves into vector-space representations. Translation and document search complete the sequence. This is a mathematical and programming-based introduction to NLP methods, with more substantial prerequisites than a general AI-awareness course.

What you will learn

• Build a sentiment classifier using logistic regression.
• Compare a naïve Bayes approach with other classifiers.
• Represent text with vectors for search or translation.

Who this suits

Intermediate Python developers with machine-learning knowledge and comfort with calculus, linear algebra and statistics, including experience with deep-learning frameworks.

Before you enroll

Check the programming environment and prerequisite mathematics. Evaluate a classifier on held-out examples and inspect errors rather than judging it from a few successful demonstrations.

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Put it into practice

Compare two sentiment classifiers on the same small dataset. Inspect several disagreements and explain how vocabulary or context affects the predictions.

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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