Gen AI Foundational Models for NLP & Language Understanding

Overview

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.

Syllabus

Fundamentals of Language Understanding

In this module, you will explore the foundational techniques and tools that enable machines to understand and process human language. You will learn about one-hot encoding, bag-of-words, embeddings, and embedding bags. You’ll begin by converting text into numerical features, move into document categorization using TorchText, and continue through to model training with PyTorch. The module also introduces you to language modeling using N-Gram models, both statistically and through neural networks. The hands-on labs will reinforce your learning by walking you through implementations in Python using PyTorch and related libraries.

Word2Vec and Sequence-to-Sequence Models

In this module, you will explore advanced neural techniques for language representation and understanding. You’ll begin by learning how Word2Vec models capture word semantics using context-based prediction. Then you’ll transition into sequence-to-sequence modeling with recurrent neural networks (RNNs) and encoder-decoder architectures, which enable tasks like translation. You’ll also investigate how to evaluate generated text using established NLP metrics and reflect on ethical concerns surrounding word embeddings. The labs will provide hands-on practice with Word2Vec integration and sequence models. In addition, the comprehensive cheat sheet and glossary will serve as quick-reference tools to reinforce your understanding of key models and concepts.