Gen AI Foundational Models for NLP & Language Understanding
Completed by Matthew Graham
March 30, 2026
9 hours (approximately)
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What you will 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 will gain
- Category: Model Evaluation
- Category: Natural Language Processing
- Category: Text Mining
- Category: Generative Model Architectures
- Category: PyTorch (Machine Learning Library)
- Category: Transfer Learning
- Category: Classification Algorithms
- Category: Large Language Modeling
- Category: Generative AI
- Category: Model Training
- Category: Feature Engineering
- Category: Data Ethics
