Work with natural language tools and techniques to solve real-world problems. This book focuses
on how natural language processing (NLP) is used in various industries. Each chapter describes
the problem and solution strategy then provides an intuitive explanation of how different
algorithms work and a deeper dive on code and output in Python. Practical Natural Language
Processing with Python follows a case study-based approach. Each chapter is devoted to an
industry or a use case where you address the real business problems in that industry and the
various ways to solve them. You start with various types of text data before focusing on the
customer service industry the type of data available in that domain and the common NLP
problems encountered. Here you cover the bag-of-words model supervised learning technique as
you try to solve the case studies. Similar depth is given to other use cases such as online
reviews bots finance and so on. As you cover the problems in these industries you'll also
cover sentiment analysis named entity recognition word2vec word similarities topic modeling
deep learning and sequence to sequence modelling. By the end of the book you will be able to
handle all types of NLP problems independently. You will also be able to think in different
ways to solve language problems. Code and techniques for all the problems are provided in the
book. What You Will Learn Build an understanding of NLP problems in industry Gain the know-how
to solve a typical NLP problem using language-based models and machine learning Discover the
best methods to solve a business problem using NLP - the tried and tested ones Understand the
business problems that are tough to solve Who This Book Is For Analytics and data science
professionals who want to kick start NLP and NLP professionals who want to get new ideas to
solve the problems at hand.
