Gen AI Foundational Models for NLP & Language Understanding

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 sema

• 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