Example Concept: Fine-tuning BERT for sentiment analysis involves several key steps. First, you need to preprocess your dataset, which includes tokenizing the text data using BERT's tokenizer and converting it into input features that BERT can understand. Next, you set up the BERT model with a classification head, which is a simple feed-forward neural network added on top of BERT to predict sentiment labels. Finally, you train the model on your dataset, typically using a library like Hugging Face's Transformers, adjusting hyperparameters such as learning rate and batch size to optimize performance.