CODeepLearning.AI · on Coursera
Natural Language Processing with Probabilistic Models
Intermediate English Professional CertificateFREE
What you'll learn
Build an autocorrect system with probabilistic methods
Use N-gram language models for autocomplete
Apply hidden Markov models to part-of-speech tagging
Understand Word2Vec word embeddings
Use dynamic programming in NLP tasks
Implement the models in Python
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
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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 Google Brain research scientist and co-author of the Transformer paper and TensorFlow. The pairing brings both clear teaching and frontier research depth.
Requirements
- Basic ML (cost functions, optimisation, neural-net basics)
- Comfortable with Python
Who this course is for
- Learners building NLP fundamentals
- Data scientists moving into NLP
- Anyone in the NLP Specialization
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
4.6 trust score
Frequently asked questions
Basic machine learning — cost functions, optimisation, and neural-network basics — plus Python. It's Course 2 of the NLP Specialization, so the first course also helps.
Yes — these probabilistic foundations (language models, HMMs, embeddings) underpin how modern systems represent and process language, so they make later transformer work click.
You can audit the full course free on Coursera. A certificate and graded labs require a subscription.
Yes — you build autocorrect, autocomplete, and a part-of-speech tagger in Python, which makes the methods concrete.
Younes Bensouda Mourri and Łukasz Kaiser — the latter a co-author of the Transformer paper — so the research depth is real.