Transformer Models for NLP: Getting Started with BERT and GPT

Transformer models have revolutionized how computers understand human language, powering everything from search engines to conversational AI. If you want to understand how technologies like BERT and GPT actually work under the hood, starting with the core concepts is essential. This written course guides you through the fundamental mechanics of transformer architectures, natural language processing (NLP), and basic computer vision applications. You will transition from a beginner to someone who can confidently explain, analyze, and apply transformer-based models to textual data. What you'll learn: Understand the core architecture of transformer models, including self-attention mechanisms; Analyze how models like BERT and GPT process and generate natural language; Explore foundational applications of transformers in text classification, translation, and computer vision; Apply pre-trained models to solve real-world natural language processing tasks; Discover modern implementation patterns, including basic prompt engineering and model fine-tuning concepts. You will begin with essential terminology and the history of NLP before diving into self-attention, encoder-decoder frameworks, and practical text-processing workflows. Through clear explanations and conceptual code snippets, you will build a solid understanding of modern language models. This course is designed for aspiring data scientists, software developers, and tech enthusiasts who are new to NLP and deep learning, with no prior experience with transformer models required. Start reading today to unlock the power of modern transformer architectures.