CODeepLearning.AI · on Coursera
Natural Language Processing Specialization
Advanced 200 hours English Specialization Certificate Certificate
Editor's Pick
Understanding transformers from first principles is increasingly essential for ML engineers — this is the most rigorous NLP curriculum on Coursera.
About this course
This four-course specialization covers NLP from classical ML through modern deep learning: sentiment analysis, word embeddings, sequence models with LSTMs, and finally the transformer architecture that underpins BERT and GPT.
Łukasz Kaiser, co-author of the original Attention Is All You Need paper, teaches the transformer module — giving students unmatched insight into how modern LLMs are architected.
What you'll learn
Build text classifiers using logistic regression, naive Bayes, and word vectors
Implement Word2Vec and GloVe word embeddings
Build sequence models with LSTMs, GRUs, and Siamese networks
Implement the attention mechanism and transformer from scratch
Apply neural machine translation and question answering with transformers
This course includes
200h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Instructor
YB
Younes Bensouda Mourri / Łukasz Kaiser
Coursera instructor
190K+ learners4 courses4.6 instructor rating
Taught by Younes Bensouda Mourri and Łukasz Kaiser (co-author of Attention Is All You Need) from DeepLearning.AI.
Requirements
- Python, NumPy, and basic ML knowledge; calculus and linear algebra helpful
Who this course is for
- ML engineers who want deep understanding of NLP and transformers
- Data scientists working with text data at scale
- Researchers who want to understand how LLMs are built
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
Frequently asked questions
It covers the transformer architecture underlying both — you'll understand how they work architecturally.
Comparable difficulty — assumes ML fundamentals with added domain-specific NLP concepts.