Named Entity Recognition with BERT

Extracting unstructured information from text is a core challenge in modern artificial intelligence. This text-only course provides a clear, step-by-step pathway to understanding and implementing Named Entity Recognition (NER) using state-of-the-art transformer models. You will start by exploring the foundational concepts of tokenization, sequence labeling, and the architecture of pre-trained models before moving on to practical implementation. By reading through structured explanations and analyzing clear code examples, you will gain the skills needed to prepare text datasets, configure transformer architectures, and train models to identify custom entities like names, locations, products, or dates in raw text. What you'll learn: - Understand the core concepts of Named Entity Recognition and sequence labeling tasks - Prepare and format custom text datasets using modern tokenization techniques - Fine-tune pre-trained BERT models specifically for custom entity extraction - Apply evaluation metrics like precision, recall, and F1-score to assess model performance - Implement modern PyTorch training loops and leverage Hugging Face libraries - Handle common real-world challenges such as class imbalance and subword token alignment The course begins with essential terminology and the mechanics of transformer-based language models. From there, you will progress to data preprocessing, model configuration, fine-tuning, and evaluating your trained NER model on practical datasets. This course is designed for software developers, data analysts, and aspiring machine learning engineers who want to learn natural language processing from the ground up. No prior experience with transformers or deep learning is required, though a basic familiarity with Python is helpful. Start reading today to build your own custom text extraction pipelines.