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学生对 IBM 提供的 Generative AI and LLMs: Architecture and Data Preparation 的评价和反馈

4.6

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313 个评分

课程概述

Ready to explore the exciting world of generative AI and large language models (LLMs)? This IBM course, part of the Generative AI Engineering Essentials with LLMs Professional Certificate, gives you practical skills to harness AI to transform industries. Designed for data scientists, ML engineers, and AI enthusiasts, you’ll learn to differentiate between various generative AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll also discover how LLMs, such as generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT), power real-world language tasks. Get hands-on with tokenization techniques using NLTK, spaCy, and Hugging Face, and build efficient data pipelines with PyTorch data loaders to prepare models for training. A basic understanding of Python, PyTorch, and familiarity with machine learning and neural networks are helpful but not mandatory. Enroll today and get ready to launch your journey into generative AI!...

51 - Generative AI and LLMs: Architecture and Data Preparation 的 66 个评论（共 66 个）

创建者 Shubham K

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Jul 30, 2025

I would expect more hands on and code submissions

创建者 Muhammad A A

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Mar 13, 2025

It was good introductor course

创建者 Aswani K V

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Jan 4, 2025

very useful for beginners

创建者 Justin R

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Oct 26, 2024

The content in the lectures is complex but the slides are not made available to download. Also the Cheat Sheets and other similar materials are presented in weird "windows" that also do not make them available for download. This is a first for me in a Coursera course and I'm find it not very conducive to learning. These material should be easily available. Not certain I will complete the full Specialization if the materials are not made available.

创建者 Yongchang L

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Jul 14, 2024

I found the course on LLMs to be a solid introduction, particularly appreciating the cheatsheet and experiments included. However, the requirement to purchase a $49 certificate to complete the course felt excessive. The course producer should learn from many other courses on Coursera, completing the course should be free with the option to purchase the certificate as an add-on.

创建者 Uday T

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Aug 25, 2025

There are some lab works where training was consuming enormous time, due to which I was not able to complete that lab. This needs to be resolved so that other learner would get benefit out of this.

创建者 fidel m

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Feb 9, 2025

so much of reading material and so less of actual videos. the speaking voice in video is also in a rush

创建者 Jimmy M

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Mar 22, 2025

Content was decent for intermediate intro, but all labs were broken. Easy 4 stars if they worked.

创建者 Sailesh M

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Jan 16, 2025

Labs don't work as torchtext is deprecated and doesn't run on Python 3.12 kernel

创建者 Kajal V

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Sep 1, 2025

some module are lock and those are important for learning

创建者 Jochen G

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Mar 20, 2025

The course is not well maintained, and rather superficial

创建者 AYA A

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Jul 29, 2025

need live coding labs to test out the scripts

创建者 Jonas K

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Aug 26, 2025

It basically just consists of Jupyter Notebooks without much explanation. I tried to understand the content and implement it on my own because the exercises did not help me understand it at all. As a result, it took me much longer than expected to work through the content, which disrupted my schedule. I would not recommend this course. Unfortunately, I am not aware of any better alternatives for learning about this topic.