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Introduction to Large Language Models

This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

状态:Prompt Engineering
状态:Large Language Modeling
初级课程小时

精选评论

JW

4.0评论日期:Jun 30, 2024

Engaging and easy to follow content. Good resource materials provided for further reading.

LD

4.0评论日期:Sep 15, 2024

Very detailed and well-structured training. It would be great if it could be more informative and practical. Thank you.

FA

5.0评论日期:Mar 17, 2024

Quick way to learn about large language models. Helped me understood the basic concepts.

SM

5.0评论日期:Jan 9, 2025

Great course with detailed info for people who are beginners, know a bit or know a lot.

SJ

5.0评论日期:Mar 2, 2026

I love how each section is broken down and explained into bite size lessons so that you do not get overwhelmed.

MS

4.0评论日期:Sep 6, 2023

Very elementary and short course. It is biased towards Google products, but that's expected. Overall a good course!

KS

5.0评论日期:Mar 28, 2025

I was really happy with the course, I understood the concepts really well. Thank you so much

NM

5.0评论日期:Aug 5, 2024

As a beginner, the approach by Coursera and Google was effective. Highly recommended!

UA

5.0评论日期:Oct 14, 2023

I believe that John Ewald did a great job at covering the basics for absolute beginners. I will recommend others to check out the course too before diving deep into more specific topics.

CB

5.0评论日期:Feb 13, 2025

The LLM course offers a comprehensive and practical understanding of large language models, equipping learners with essential skills for AI-driven text generation, analysis, and application.

PK

5.0评论日期:Sep 24, 2023

a very short and precise to know the basic of llm. the session is quite interesting with easily understandable pictures and interactive slides.

NM

5.0评论日期:Sep 7, 2024

The instructor was great and the transcripts, graphs, and reading material were invaluable. Thank you for this opportunity!

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