Natural Language Processing with Probabilistic Models

DeepLearning.AI uses familiar language tasks to introduce probabilistic methods. Autocorrect leads into hidden Markov models for tagging, followed by autocomplete and neural word representations. The sequence helps learners connect a model’s assumptions with the behavior of a text-processing application.

What you will learn

• Model an autocorrect or autocomplete task probabilistically.
• Use hidden Markov ideas for part-of-speech tagging.
• Explain how word embeddings represent relationships between words.

Who this suits

Learners with machine-learning knowledge, intermediate Python and deep-learning-framework experience, plus calculus, linear algebra and statistics.

Before you enroll

Check the assumed probability knowledge and coding setup. A model’s ranking of possible words depends on the data and language context; preserve a separate evaluation set.

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Put it into practice

Create a small autocomplete evaluation with both common and unusual phrases. Compare the suggestions and record which training-data assumptions explain the failures.

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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