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DeepLearning.AI · on Coursera

Natural Language Processing with Classification and Vector Spaces

Intermediate English SpecializationFREE
SkillsNatural language processingSentiment analysisWord embeddingsVector spacesLogistic regressionPythonNumPy

Is this course right for you?

Our take
DeepLearning.AI's entry point into NLP, and one that insists you understand the machinery rather than just call it.

Good for: Understanding classical NLP from the ground up, with hands-on Python.

Skip if: You want a quick, library-based NLP tutorial or you are new to Python and maths.

You implement sentiment analysis, work directly with vector spaces and word embeddings, and code the ideas yourself instead of importing a finished library — which is exactly what builds real intuition for how machines handle language. That rigour sets the bar for who it suits: you'll want solid Python and some maths, so steer clear if you wanted a quick library-based tutorial or you're still finding your feet in either.

It's worth knowing where this sits in 2026: NLP has moved heavily toward large language models, and this course deliberately teaches the durable foundations — embeddings, vector spaces — with the transformer techniques coming later in the four-course specialisation. Free to audit on Coursera; the certificate needs a subscription or Coursera Plus, with financial aid, and it's taken seriously in ML circles as proof you did the work (as of 2026).

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About this course

Natural Language Processing with Classification and Vector Spaces is the first course of DeepLearning.AI's NLP Specialization, and it builds real intuition for how machines work with language. You implement sentiment analysis with logistic regression and naive Bayes, then move into vector spaces — representing words as numbers and even building a simple machine-translation step using word embeddings.

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Instructor

YB
Younes Bensouda Mourri
Coursera instructor

Taught by Younes Bensouda Mourri (Stanford, DeepLearning.AI) and Łukasz Kaiser, a co-author of foundational deep-learning and transformer research. The pairing brings both teaching clarity and serious research depth to the material.

Frequently asked questions

Yes — it is course one of DeepLearning.AI's four-course NLP specialization. It introduces the foundations of natural language processing, and the later courses build toward probabilistic models, sequence models, and finally attention and transformers. You can take this alone for the introductory material, but it is designed as the opening of that sequence, which together covers NLP from basics to modern architectures.

Not this course — it covers the foundations: text classification (like sentiment analysis) and representing words as vectors in vector spaces, using classical machine-learning approaches. Transformers and the architectures behind large language models come in the specialization's final course. So this builds the groundwork and intuition, with the modern LLM-relevant techniques taught later in the sequence.

Yes, as a foundation. Even in an LLM-dominated world, understanding how text is represented as vectors and classified builds intuition that makes modern models far less mysterious. It is introductory rather than state-of-the-art, so pair it with transformer-focused learning, but the fundamentals of representing and classifying language remain a genuinely useful starting point rather than obsolete knowledge.

Yes, and some machine-learning grounding. It involves Python programming assignments and assumes you can already code in Python and understand basic ML ideas. It is not a beginner's introduction to either. Comfortable Python plus familiarity with core machine-learning concepts is the right preparation; without them, the assignments become a struggle with syntax rather than a focus on NLP.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded programming assignments and the certificate require a subscription, with financial aid available. Since the hands-on coding is central to learning NLP, the paid track is worth considering if you want that practice rather than only watching the lectures.
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