This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA ("state of the art"). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included (available for downloading with Amazon proof of purchase by writing to [email protected]).




This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA ("state of the art"). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included (available for downloading with Amazon proof of purchase by writing to [email protected]).

Features
+Covers extensive topics related to natural language processing
+Includes separate appendices on regular expressions and probability/statistics
+Features companion files with source code and figures from the book (available for downloading with Amazon proof of purchase by writing to [email protected])

Brief TOC
1: Working with Data. 2: NLP Concepts (I). 3: NLP Concepts (II). 4. Algorithms and Toolkits (I). 5. Algorithms and Toolkits (II). 6: NLP Applications. 7: Transformer, BERT, and GPT. Appendices. A: Introduction to Regular Expressions. B: Introduction to Probability and Statistics.

About The Author
Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, NLP, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the Data Science Fundamentals Pocket Primer (all Mercury Learning).