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

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


Instructors: Joseph Santarcangelo +1 more
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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: Artificial Neural Networks
- Category: Model Optimization
- Category: Feature Engineering
- Category: Model Training
- Category: Large Language Modeling
- Category: Model Evaluation
- Category: Transfer Learning
- Category: Natural Language Processing
- Category: Responsible AI
- Category: Generative Model Architectures
- Category: Embeddings
- Category: Text Mining
- Category: Data Ethics
Tools you'll learn
- Category: Generative AI
- Category: PyTorch (Machine Learning Library)
- Category: Classification Algorithms
Details to know

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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.