Blueprints for Text Analysis using Python: Machine Learning Based Solutions for Common Real World (NLP) Applications Paperback – 31 Dec. 2020

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.

I can't recommend this book highly enough. It provides an excellent foundation for understanding the fundamentals of NLP.I'd suggest starting here before moving onto the more complex Deep Learning techniques that are making lots of headlines these days. For many business applications you don't actually need state-of-the-art techniques - you need the fundamentals, and this book quite literally gives you the blueprints for applying them. In fact it also covers some more classical ML techniques and really useful concepts like explaining your classifier and word embeddings, so it's not just the basics.I particularly like the way that you can run the accompanying notebooks on Google Colab, meaning that you don't need to faff around setting up your environment and dealing with Python dependencies etc. All books should provide this option!Bravo to the author.