CODeepLearning.AI · on Coursera
Generative AI with Large Language Models
Intermediate English Professional CertificateFREE
Editor's Pick
The clearest hands-on bridge from 'I've used ChatGPT' to actually building with LLMs — practical labs, taught by people who ship this in production.
What you'll learn
Explain how transformer-based LLMs are trained and generate text
Frame a generative-AI use case and pick the right model approach
Fine-tune a foundation model for a specific task
Apply RLHF to align a model with human preferences
Reason about the cost/performance trade-offs of each approach
Understand how LLMs are deployed and scaled in production
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
Compare alternatives for Generative AI with Large Language Models
Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
COCoursera—(0)
Generative AI with Large Language Models
- Price
- FreeAudit free · Certificate available
- Duration
- —
- Level
- Intermediate
- Certificate
- Professional
COCoursera4.9(78,000)
Machine Learning Specialization
- Price
- FreeAudit free · Cert $49/mo
- Duration
- 94 hrs
- Level
- Intermediate
- Certificate
- Professional
COCoursera4.8(12,400)
AWS Certified AI Practitioner
- Price
- FreeAudit free · Cert $49/mo
- Duration
- 14 hrs
- Level
- Beginner
- Certificate
- Professional
COCoursera4.8(24,100)
Generative AI for Everyone
- Price
- FreeAudit free · Cert $49
- Duration
- 6 hrs
- Level
- Beginner
- Certificate
- Professional
Prices and ratings refreshed daily. We're not affiliated with any single provider.
Instructor
AB
Antje Barth
Coursera instructor
— learners— courses — instructor rating
Led by Antje Barth, a Principal Developer Advocate for generative AI at AWS and co-author of 'Data Science on AWS,' alongside fellow AWS specialists. The teaching team brings production experience rather than pure theory, which shows in the labs.
Requirements
- Comfortable with Python
- Basic ML familiarity helps but isn't strictly required
Who this course is for
- Python developers moving into generative AI
- Data scientists who want hands-on LLM skills
- Engineers evaluating fine-tuning vs prompting for a project
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
Comfort with Python is the main one. You don't need prior NLP experience — the course builds up the LLM concepts — though a little ML familiarity makes the fine-tuning sections smoother.
For developers, it's one of the most recommended LLM courses precisely because it's hands-on: you fine-tune and evaluate real models rather than just watching lectures. If you want practical LLM skills, it delivers.
It covers advanced topics but stays approachable for anyone with Python. The labs are the challenging-but-rewarding part — RLHF and fine-tuning take focus, but the material is paced for non-experts.
The LLM lifecycle: transformer fundamentals, prompting, fine-tuning, reinforcement learning from human feedback (RLHF), and deployment considerations — with hands-on labs throughout.
You can audit the full course free on Coursera. A certificate (and graded labs) requires a Coursera subscription.