Fundamentals of Natural Language Processing

Colorado Boulder develops language processing through probabilistic models, classification and vector-space semantics. Logistic regression and word embeddings give the course concrete modeling examples. It suits learners who want to understand core NLP representations before moving into larger transformer-based systems.

What you will learn

• Explain a probabilistic language model.
• Build a text-classification approach using logistic regression.
• Compare vector-space semantics and word embeddings.

Who this suits

Intermediate learners proficient in Python, data structures and fundamental algorithm-design topics.

Before you enroll

Preview the mathematical assumptions and programming exercises. Compare a model with a simple baseline and preserve examples that reveal ambiguity or vocabulary limitations.

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Put it into practice

Classify a small text dataset with a simple baseline and a learned model. Explain whether the improvement is meaningful and identify one recurring error type.

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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