Generative AI and LLMs: Architecture and Data Preparation
Completed by 震阳 王
January 8, 2026
5 hours (approximately)
震阳 王's account is verified. Coursera certifies their successful completion of Generative AI and LLMs: Architecture and Data Preparation
What you will learn
Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models
Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks
Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer
Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets
Skills you will gain
- Category: Responsible AI
- Category: Large Language Modeling
- Category: Natural Language Processing
- Category: Generative AI
- Category: LLM Application
- Category: Data Pipelines
- Category: Model Training
- Category: PyTorch (Machine Learning Library)
- Category: Artificial Intelligence
- Category: Recurrent Neural Networks (RNNs)
- Category: Data Preprocessing
- Category: Generative Adversarial Networks (GANs)
