Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications 1st Edition

I noticed this in my bookshelf last week, and apparently I had bought it and forgotten to even take a look. I started skimming and was surprised at the usefulness of this book. It is very good.Pros:It does an excellent job of explaining fundamentals and common workflows of NLP. I know this, because my work is primarily NLP. It covers fundamentals quite well, such as tokenization, word vextors, similarity, classification; topic modeling, etc. It also gets into topic modeling. Later, there is a whole chapter about explainability of NLP models, which I am excited to read. I adore NLP insights.Cons:Like cookbooks, most things are blueprints. That’s very useful if you like those kinds of boxy explanations. I personally prefer typical book format, but this works too. I just find it a little distracting.But the book is rare in that it really explains the fundamentals. Many books junp straight to ML, or are only ML. This is good for foundation. It is also really useful and practical. This is now in my top four favorite NLP books. The pros absolutely outweigh the cons. And the datasets seem wonderful.I’m still reading and learning from this. Really glad I noticed I forgot to read.

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.

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 have been studying this book with the greatest interest. I have programming and data analysis experience, but I was new to text analytics and needed a practical introduction. I highly recommend this book to anyone in a similar situation. The book offers a well-structured and problem-driven introduction to the topic of text analytics with a good pace on the learning progress. There are several practical and useful programming examples. The theoretical foundation for the used methods is briefly introduced giving some clues on the mathematics lying behind the introduced methods without theoretical details. The supporting online materials could easily run on my computer. Overall, I think the authors have done a magnificent job helping newcomers into the practical aspects of text analytics with Python.

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!