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.

Gen AI Foundational Models for NLP & Language Understanding
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Gen AI Foundational Models for NLP & Language Understanding
This course is part of multiple programs.


Instructors: Joseph Santarcangelo +1 more
34,984 already enrolled
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206 reviews
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What you'll learn
Explain how one-hot encoding, bag-of-words, embeddings, and embedding bags transform text into numerical features for NLP models
Implement Word2Vec models using CBOW and Skip-gram architectures to generate contextual word embeddings
Develop and train neural network-based language models using statistical N-Grams and feedforward architectures
Build sequence-to-sequence models with encoder–decoder RNNs for tasks such as machine translation and sequence transformation
Skills you'll gain
- Category: Data Ethics
- Category: Model Optimization
- Category: Generative Model Architectures
- Category: Model Evaluation
- Category: Natural Language Processing
- Category: Transfer Learning
- Category: Large Language Modeling
- Category: Text Mining
- Category: Embeddings
- Category: Model Training
- Category: Artificial Neural Networks
- Category: Feature Engineering
- Category: Responsible AI
Tools you'll learn
- Category: PyTorch (Machine Learning Library)
- Category: Classification Algorithms
- Category: Generative AI
Details to know

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There are 2 modules in this course
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Reviewed on Oct 26, 2025
The lab materials are very complicated could be made abstract using tensorflow.
Reviewed on Mar 25, 2025
Super course,.. labs are too good to learn and challenging too.
Reviewed on Jan 28, 2026
AI Foundational and LLMs is learning career growth.