Natural Language Processing with Classification and Vector Spaces
Intermediate English SpecializationFREE
Our recommendation
The first course of DeepLearning.AI's Natural Language Processing Specialization on Coursera. It builds real intuition for how machines work with language: you implement sentiment analysis, work with vector spaces and word embeddings, and code the ideas yourself rather than calling a library. Rigorous and hands-on, but it expects solid Python and some maths.
Good for: Understanding classical NLP from the ground up, with hands-on Python.
Less suitable if: You want a quick, library-based NLP tutorial or you are new to Python and maths.
Skills you'll gain
Natural language processingSentiment analysisWord embeddingsVector spacesLogistic regressionPythonNumPy
Is this course right for you?
A good fit if you…
You want to understand how NLP actually works
You are comfortable with Python and some maths
You are building toward an NLP or ML role
Consider something else if you…
You want a quick plug-and-play NLP tutorial
You are new to Python
You want the latest transformer/LLM techniques as the main focus
Requirements: Solid Python, and comfort with linear algebra and basic probability.
Realistic time: Around 25–30 hours over a few weeks.
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.
What you'll learn
Build a sentiment classifier with logistic regression
Apply naive Bayes to text classification
Represent words as vectors and reason about vector spaces
Use word embeddings to capture meaning
Implement a basic word-translation step with embeddings
Code core NLP algorithms in Python rather than calling a library
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
Free to audit on Coursera. A certificate needs a Coursera subscription (about $49/month) or Coursera Plus, with financial aid available. It is the first of four courses in the NLP Specialisation.
Comparison · LBS
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COCoursera
Natural Language Processing with Classification and Vector Spaces
Price
Free
Audit free · Certificate available
Duration
—
Level
Intermediate
Certificate
Specialization
COCoursera
Natural Language Processing with Probabilistic Models
A DeepLearning.AI certificate on Coursera is well respected in the ML community as evidence of the work, though it is a course certificate rather than a formal qualification.
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.
Yes. You implement the methods yourself, so solid Python plus some linear algebra and probability are expected.
You can audit it free on Coursera; the certificate requires a subscription or Coursera Plus.
This first course focuses on classical foundations like embeddings and vector spaces. Transformer and attention-based methods come in later courses of the specialisation.
Yes — it is the first of four courses in DeepLearning.AI's NLP Specialisation.
Yes. The foundations it teaches underpin modern NLP, even though day-to-day work increasingly uses large language models.