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Learner Reviews & Feedback for Generative AI with Large Language Models by DeepLearning.AI

4.8
stars
3,504 ratings

About the Course

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications. By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology. This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI....

Top reviews

MB

Jan 4, 2024

This course has been an extra addition in enhancing my understanding of the Generative AI project lifecycle, particularly in the context of architecture and implementation strategies.

SM

Oct 12, 2024

Pretty good overview for Product Managers and leaders who are interested in learning about Generative AI with hands-on labs that are not too detailed, yet help you develop the intuition.

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776 - 800 of 841 Reviews for Generative AI with Large Language Models

By Philipp G

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Aug 14, 2025

Excellent course 🚀, just a bit dated today (2025)

By Brian R

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Aug 17, 2023

Great course. The lab 3 instance died a few times.

By Enrique C

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Jul 3, 2023

Very good in general. Thanks Coursera and AWS!

By Noah A

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Nov 10, 2023

Good survey course. Not much hands-on work.

By Rishav R

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Dec 29, 2023

More hands-on exercise should be made.

By Gaurav S

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Apr 20, 2024

Good Course, lots of content

By Sergio G

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Jul 23, 2023

Maybe is necessary more labs

By Mohd R

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Apr 3, 2024

great course to learn genai

By Parth T

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May 23, 2024

good introductory course

By Viswanatha r

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Jan 16, 2024

Good foundational course

By Vineet S

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Nov 11, 2025

It's good for beginner.

By Jeffrey P N

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Jul 20, 2023

Lacks of more exercises

By Biswajit K

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Aug 19, 2024

fully satisfied.

By Sameer M

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Aug 5, 2023

very informative

By mahdi a n

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Feb 7, 2024

5 is for god

By Swarupa K

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Nov 7, 2025

very useful

By Bharath V G

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Aug 21, 2025

good course

By ARJUN K

•

Jul 6, 2023

good one!

By Wayne

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Dec 28, 2023

great

By Raj D D

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Sep 12, 2024

nice

By Sandhya G

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Feb 27, 2024

Good

By xingnan z

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Jan 8, 2024

good

By Kamal M

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Oct 19, 2023

good

By Lakshmi G

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Sep 10, 2023

Good

By Freddie K

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Jan 31, 2024

"Intermediate" level in the sense that you perhaps need some basic understanding of machine learning, but this is definitely not a course that challenges you. You get a very high level conceptual explanation of basic concepts (including things like LoRA, RLHF), but definitely no specifics on the implementation level. The assignment "Labs" consists of executing pre-written code in notebooks, and seeing the result output. No coding of your own, and typically just making function calls to Huggingface libraries, but not actually seeing how the algorithms are implemented.