Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications

Exceptional introduction to NLP using Python and spaCy. This is a great book for working programmers who are looking to learn NLP by applying examples which are much more developed than those found in other cookbook texts. Unlike older Python books this skips NLTK and this focuses on modern libraries and tools which are more robust for production use.If you want to get an idea of the content before purchasing, you should review the chapter by chapter source code and notebooks on GitHub. They

This book covers the key areas of text analytics and it is a great textbook for a Natural Language Processing course. It is for advanced students who understand the key concepts in Machine Learning and have a good command of Python.

I will start with a big thanks to the book authors for sharing their NLP wisdom. I have learned a lot from this book and ended up implementing the learnings at work (daily tickets) to my Computer Science Master's project. the chapters of 4,5,6, 8,9,10 were most used chapters in my use case. the blueprints are well explained and well documented. One recommendation is to download their code from GitHub and start using your own data to see the real magic.

There are literally 100s of books on this subject, but this is one of the best to date. Very practical - not just one off scripts, but entire recipes that can be easily adapted to real-world demands on actual projects. Uses common libraries like spaCy that practitioners in this space are likely using already, or should be using if they are not. I rarely read books like this from cover-to-cover; generally a chapter is sufficient - but this book was an exception. Highly recommended.

This book delivers on what it sets out to do: provide ready-made code blueprints for various NLP tasks using Python. It was straightforward about its mission and didn't promise to make me "an expert in 11 short chapters!". And for that I am grateful.It is the most up-to-date resource that I've come across that implements some of the latest advancements in the literature of NLP.The second half of the book is much better than the first, which is mostly basic stuff that most people with some NLP exposure would be well-acquainted with. But they are perfectly paced for any beginner. It is a great start.Chapters 4-11 cover name-entity-recognition, sentiment analysis, knowledge graphs, LDA models, text summarization, and a lot more. What's more, the code just works which is something that is not always the case with other publishers. The code snippets are very borrowable, which is a plus.Highly recommended!

What distinguishes this book from others in the same area is that all recipes included lead to an actual insight at the end, authors don't forget the so what behind the code and the analysis. Recipes are combined with some theory background too to help build intuition about the key concepts, which is useful. I come from an R background, but found the Python code easy to follow (use of many familiar data frame concepts).

I feel compelled to write this review to acknowledge the authors for their brilliant work.I am particularly impressed that this book is so well supported on github with colab links. The documentation and code is all so good.

Every chapter in this book is practical and can be replicated into your programming environment on your local machine at least. This book teaches us the powerful methods to analyse and process text data which will get at least 80% of the job done. Anyone with basic programming experience can benefit from this book. The best book to date on NLP.

I really enjoyed reading this book as it comprehensively and clearly explains the fundamentals of NLP, while also putting it directly into practice.