Named Entity Recognition (NER) is a natural language processing technique that identifies and extracts essential entities such as names, locations, and organisations from text-based data. Initially conceptualized at the Message Understanding Conference, NER has gained widespread use across various sectors, including business and medicine, to automate information extraction processes. The article explores the historical development of NER, the use of modern frameworks like PyTorch and TensorFlow, and pre-trained models such as BERT to build NER systems. Additionally, it discusses practical applications of NER in supporting chatbots, biomedical research, document categorisation, and business data processing. The guide provides a detailed walkthrough of training a NER model using BERT, covering data acquisition, model training, and accuracy estimation. It also highlights the integration of NER with Optical Character Recognition (OCR) and deep learning for enhanced information extraction from documents. The article concludes with examples of using popular libraries like NLTK and Spacy to perform NER tasks, emphasizing its critical role in automating data extraction and management in various industries.