CODeepLearning.AI · on Coursera
Natural Language Processing with Classification and Vector Spaces
Intermediate English Professional CertificateFREE
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
Comparison · LBS
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Instructor
YB
Younes Bensouda Mourri
Coursera instructor
— learners— courses — instructor rating
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.
Requirements
- Comfortable with Python
- Basic machine learning and some linear algebra
Who this course is for
- Developers starting in natural language processing
- Data scientists adding NLP to their skill set
- Learners who want NLP fundamentals before transformers
About this provider
CO
Coursera
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4.6 trust score
Frequently asked questions
Python plus some basic machine learning and linear algebra. It's hands-on, so you'll be implementing algorithms rather than just reading about them.
Yes if you want real understanding — the fundamentals here (classification, embeddings, vector spaces) underpin how modern models represent language, so it makes later transformer work click.
You can audit it free on Coursera. The certificate and graded assignments require a subscription.
A fair amount — you implement the core algorithms in Python, which is the point. Expect to write code, not just watch lectures.
If you want depth, do this first — it builds the intuition LLM courses assume. If you only want to use LLMs practically, a course like Generative AI with LLMs is a more direct path.