Natural Language Processing (NLP) encompasses a range of techniques and algorithms that enable machines to process and analyze large amounts of natural language data. It combines linguistics, computer science, and artificial intelligence to facilitate tasks such as speech recognition, sentiment analysis, machine translation, and text summarization. NLP involves several core components, including syntax (structure), semantics (meaning), and pragmatics (context), which help machines understand the nuances of human language. Techniques such as tokenization, part-of-speech tagging, named entity recognition, and word embeddings (e.g., Word2Vec, GloVe) are commonly used in NLP applications. With the rise of deep learning, models like BERT and GPT have further advanced the capabilities of NLP, allowing for more sophisticated language understanding and generation. NLP is widely used in applications like chatbots, virtual assistants, and content recommendation systems, making it an essential field for enhancing human-computer interaction.