Mastering Generative AI: Language Models with Transformers

This intermediate-level IBM course equips you with cutting-edge skills in transformer-based language models that are driving the generative AI revolution. As demand for these technologies skyrockets, this course provides the practical knowledge employers are actively seeking in AI engineers and data scientists. You'll explore the fundamental architecture of transformers, learning about key components like positional encoding, attention mechanisms, and self-attention that enable these models to capture contextual information and dependencies in text. The curriculum covers both decoder-based models like GPT (Generative Pre-trained Transformers) for text generation and language translation, and encoder-based models like BERT (Bidirectional Encoder Representations from Transformers) for advanced language understanding. Through hands-on labs, you'll implement these concepts using PyTorch, gaining practical experience with transformer architecture for text classification, masked language modeling, next sentence prediction, and language translation. You'll learn specialized techniques including multi-head attention, masked language modeling, and causal language modeling. The course culminates in a final project addressing real-world scenarios, ensuring you develop job-ready skills that can be immediately applied to generative AI applications in just two weeks.