BERT, or Bidirectional Encoder Representation with Transformers, is a language model introduced by Google in 2018, which transformed natural language processing by achieving state-of-the-art performance in tasks like question-answering and classification. Unlike previous models, BERT employs a bidirectional transformer architecture that considers context from both directions in a sentence for extracting patterns and representations. It uses two training paradigms: pre-training on large datasets in an unsupervised manner and fine-tuning for specific downstream tasks. BERT's architecture, which includes the self-attention mechanism of transformers, allows it to understand long-term dependencies and contextual information effectively, setting it apart from earlier models like ELMo and ULM-FiT. This tutorial demonstrates how to code BERT using PyTorch, covering preprocessing, building the model, and training, while also discussing alternatives like using pre-trained models from the Huggingface library to simplify the process. BERT's ability to be fine-tuned with minimal epochs makes it a powerful tool for various NLP tasks, offering robust performance with efficient training.