Back to Gen AI Foundational Models for NLP & Language Understanding

Learner Reviews & Feedback for Gen AI Foundational Models for NLP & Language Understanding by IBM

4.5

stars

163 ratings

About the Course

This IBM course will equip you with the skills to implement, train, and evaluate generative AI models for natural language processing (NLP) using PyTorch. You will explore core NLP tasks, such as document classification, language modeling, and language translation, and gain a foundation in building small and large language models. You will learn how to convert words into features using one-hot encoding, bag-of-words, embeddings, and embedding bags, as well as how Word2Vec models represent semantic relationships in text. The course covers training and optimizing neural networks for document categorization, developing statistical and neural N-Gram models, and building sequence-to-sequence models using encoder–decoder architectures. You will also learn to evaluate generated text using metrics such as BLEU. The hands-on labs provide practical experience with tasks such as classifying documents using PyTorch, generating text with language models, and integrating pretrained embeddings like Word2Vec. You will also implement sequence-to-sequence models to perform tasks such as language translation. Enroll today to build in-demand NLP skills and start creating intelligent language applications with PyTorch....

Top reviews

Mar 6, 2025

I have enjoyed myself in this course. I can't wait to apply the skills I have learned in this class. Thank you the fantastic Team.

Mar 25, 2025

Super course,.. labs are too good to learn and challenging too.

Filter by:

26 - 28 of 28 Reviews for Gen AI Foundational Models for NLP & Language Understanding

By Serghei I

•

Jun 1, 2025

Challenging but insightful course. A solid grasp of discrete math is essential — otherwise, many concepts will be hard to follow. The course heavily relies on mathematical formulas, some of which deserve dedicated lessons. Certain coding labs use outdated libraries, which made reproducing results time-consuming due to API changes. Despite the fast pace and compressed explanations of complex topics, the course offers valuable foundational insights. Ideally, this material could be split into two or three separate courses for better depth and clarity.

By Muhammad A k

•

Jan 3, 2025

The content is good but its very difficult to understand as its not for the beginners. Explanation part is very limited and labs are very detailed making it difficult to understand.

By Ethan K

•

Aug 27, 2025

This course is soooo boring. It feels like it's written by robots for robots. I want to see humans teaching material and making it understandable, interesting, and relatable. This is just ai-slop.