Chevron Left
Back to Generative AI and LLMs: Architecture and Data Preparation

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

4.7
stars
409 ratings

About the Course

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!...

Top reviews

SK

Jul 29, 2025

I would expect more hands on and code submissions

GO

Mar 2, 2025

I love the structure and the content in this course. I can't wait applying the skills I have acquired!

Filter by:

76 - 82 of 82 Reviews for Generative AI and LLMs: Architecture and Data Preparation

By Jochen G

•

Mar 20, 2025

The course is not well maintained, and rather superficial

By AYA A

•

Jul 29, 2025

need live coding labs to test out the scripts

By Jonas K

•

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.

By Abbas

•

Mar 17, 2026

For a learning resource it has some jumps that leave the learner with missing context. The heavy use of functional programming style of code made it really hard to read and understand

By Fan Y

•

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 Ethan K

•

Aug 27, 2025

I would not recommend this course. It is basically llm-slop being used to explain llms.

By Serhii S

•

Nov 8, 2024

very superficial