Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence that deals with the interaction between computers and human (natural) languages. The goal of NLP is to make it possible for computers to understand, interpret, and generate human language in a way that is both useful and meaningful. NLP is a crucial component in many applications, including machine translation, sentiment analysis, speech recognition, and question answering systems, among others.

In recent years, NLP has seen significant advancements, largely driven by the rapid growth of deep learning techniques. With the use of deep learning models like recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer networks, NLP models have become more accurate and versatile.

This chapter will provide an overview of NLP and its key concepts, as well as introduce various techniques for solving NLP problems. We will cover important topics such as tokenization, stemming, and stop-word removal, and we will delve into more advanced techniques like word embeddings and sequence-to-sequence models. This chapter will provide the foundation for understanding NLP and its applications in the field of machine learning.