Chapter 1, Understanding the Basics of NLP, will introduce you to the past, present, and future of NLP research and applications.
Chapter 2, NLP Using Python, will gently introduce you to the Python libraries that are used frequently in NLP and that we will use later in the book.
Chapter 3, Building Your NLP Vocabulary, will introduce you to methodologies for natural language data cleaning and vocabulary building.
Chapter 4, Transforming Text into Data Structures, will discuss basic syntactical techniques for representing text using numbers and building a chatbot.
Chapter 5, Word Embeddings and Distance Measurements for Text, will introduce you to word-level semantic embedding creation and establishing the similarity between documents.
Chapter 6, Exploring Sentence-, Document-, and Character-Level Embeddings, will dive deeper into techniques for embedding creation at character, sentence, and document level, along with building a spellchecker.
Chapter 7, Identifying Patterns in Text Using Machine Learning, will use machine learning algorithms to build a sentiment analyzer.
Chapter 8, From Human Neurons to Artificial Neurons for Understanding Text, will introduce you to the concepts of deep learning and how they are used for NLP tasks such as question classification.
Chapter 9, Applying Convolutions to Text, will discuss how convolutions can be used to extract patterns in text data for solving NLP problems such as sarcasm detection.
Chapter 10, Capturing Temporal Relationships in Text, will explain how to extract sequential relationships prevalent in text data and build a text generator using them.
Chapter 11, State of the Art in NLP, will discuss recent concepts, including Seq2Seq modeling, attention, transformers, BERT, and will also see us building a language translator.
