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Learner Reviews & Feedback for Generative AI Language Modeling with Transformers by IBM

4.5

stars

146 ratings

About the Course

This course provides a practical introduction to using transformer-based models for natural language processing (NLP) applications. You will learn to build and train models for text classification using encoder-based architectures like Bidirectional Encoder Representations from Transformers (BERT), and explore core concepts such as positional encoding, word embeddings, and attention mechanisms. The course covers multi-head attention, self-attention, and causal language modeling with GPT for tasks like text generation and translation. You will gain hands-on experience implementing transformer models in PyTorch, including pretraining strategies such as masked language modeling (MLM) and next sentence prediction (NSP). Through guided labs, you’ll apply encoder and decoder models to real-world scenarios. This course is designed for learners interested in generative AI engineering and requires prior knowledge of Python, PyTorch, and machine learning. Enroll now to build your skills in NLP with transformers!...

Top reviews

Oct 10, 2024

Once again, great content and not that great documentation (printable cheatsheets, no slides, etc). Documentation is essential to review a course content in the future. Alas!

Dec 29, 2024

This course gives me a wide picture of what transformers can be.

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26 - 31 of 31 Reviews for Generative AI Language Modeling with Transformers

By 329_SUDIP C

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Dec 2, 2024

Nice Course

By Purva T

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Jul 26, 2024

good.

By Pravinkumar B A

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Nov 5, 2025

Excellent course to understand about AI/ML/GenAI. The videos are not very detailed and just the right amount to skim through the details.

By Francesco D G

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Dec 15, 2024

Maybe a little chaotics. Slides should be available.

By David C

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Jul 27, 2025

Some labs are outdated, the contents are rushed and the assessments are inadequate. Nevertheless, it provides a good-enough broad and general picture.

By raul v

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Nov 20, 2025

los archivos de python contenían errores de compatibilidad de librerías.