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Learner Reviews & Feedback for Generative AI and LLMs: Architecture and Data Preparation by IBM

4.7

stars

146 ratings

About the Course

This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the basics of using generative AI and Large Language Models (LLMs). This course is suitable for existing and aspiring data scientists, machine learning engineers, deep-learning engineers, and AI engineers. You will learn about the types of generative AI and its real-world applications. You will gain the knowledge 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 will learn the differences in the training approaches used for each model. You will be able to explain the use of LLMs, such as Generative Pre-Trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). You will also learn about the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You will be able to explain how you can use data loaders for training generative AI models and list the PyTorch libraries for preparing and handling data within data loaders. The knowledge acquired will help you use the generative AI libraries in Hugging Face. It will also prepare you to implement tokenization and create an NLP data loader. For this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required....

Top reviews

Oct 17, 2024

I am pretty much new to NLP data preparation. However this course made me comfortable with Date preparation activities.

Feb 28, 2025

Was waiting for a course like this for a long time. Very happy with it. Library installation on labs seems a bit slow

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26 - 33 of 33 Reviews for Generative AI and LLMs: Architecture and Data Preparation

By 조한슬

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Oct 30, 2024

I think it is too easy to get certification. The difficulty of the examination should be increased.

By Aswani K V

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Jan 4, 2025

very useful for beginners

By 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.

By 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.

By 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

By 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

By Fan Y

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Oct 15, 2024

Tokenizer & dataloader are quite important parts but I am surprised by how shallow they are touched and how easy are the quiz questions.

By Serhii S

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Nov 8, 2024

very superficial