In this module, you will learn about the significance of generative AI and how it is transforming various fields through content generation, code creation, and image synthesis. You will explore key generative AI architectures, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and transformers, and understand the differences in their training approaches. You’ll also examine how large language models (LLMs) like generative pretrained transformers (GPT) and bidirectional encoder representations from transformers (BERT) are applied in building NLP-based applications. Finally, through a hands-on lab, you will create a simple chatbot using the Hugging Face transformers library and get introduced to essential tools and libraries used in generative AI development.

In this module, you will learn how to prepare data for training large language models (LLMs) by implementing tokenization and building data loaders. You will explore different tokenization methods and understand how tokenizers convert raw text into model-ready input. You will implement tokenization using libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer. Additionally, you will learn the role of data loaders in the training pipeline and use the DataLoader class in PyTorch to create a data loader with a custom collate function that processes batches of text. These practical skills are essential for building efficient NLP pipelines for LLM training. In addition, supporting materials, such as a cheat sheet and glossary, will reinforce your learning.