Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

About This Book This was a highly regarded book when it came out in the summer of 2020, and I can see why. It's the generalist's guide to NLP, a high-level overview of the entire field. If you want to learn about natural language processing, this is where you start. There are books to read after this one, but this is your foundation. Who Is This For? The authors intent this book to be for anyone who is interested in or working in the field. However, they also have a list of prerequisites they want you to have to gain the maximum value from the book: an intermediate level of Python, knowledge of the general software development life cycle, a basic knowledge of machine learning, and some general knowledge of what NLP is. I can see where these can be useful, but I also agree with the authors that these are not completely necessary. Why Was This Written? The authors wrote this book to fill a perceived gap between what previous books offered versus what they think is required for working with natural language in production. From what I have seen from other books, I can agree. Organization The macrostructure of this book is well thought out and is broken up into four parts: foundations, essentials, applied, and bringing it all together. I really like these sections and how they include a map of reading order in the preface. This is useful when you already have a little bit of knowledge and want to see how best to skip around without missing anything. Within the chapters, each one is different, but all end with a wrapping up section to summarize the chapter and give all the references. Having a summary at the end is great for helping the reader retain the information they just read. I never understood why some technical books leave this out. Did This Book Succeed? Yes, very much so. This is a foundational book for anyone learning natural language processing that paves the way for more detailed books when you want to drill down and specialize. It's well laid out and easy to navigate. This should be on the bookshelf of anyone working in artificial intelligence. Rating and Final Thoughts This book is step one to learning natural language processing and has earned all of the accolades it has been given. Buy this book and read through it to have a thorough understanding of the field without getting stuck in the details. I give this book a 5 out of 5. This should be in your library.

First, a little bit about my background. I have been working in silicon valley for the past decade for a smattering of companies (both big and small). I am the typical software engineer who decided to switch to machine learning and have been building recommendation systems for the last 5 years. In fact, NLP is front and center in my current company (Hint: it is where you probably read most of your data science articles on the web) and this is where I found that the utility of the book shines through. What really stood out to me is how well this book has been laid out coaxing a beginner through various aspects of language and then leading them through the complex technical aspects with tremendous lucidity. The code samples serve to further reinforce these concepts as the authors seem to understand that the best way to really learn something is by doing. In my experience reading articles and books on ML/NLP, there are a lot of assumptions about the reader. I found it refreshing that this book makes no such assumptions. How many books do you know that actually explain what an "embedding" is? That being said, one of the major strengths of this book is that it effortlessly combines theoretical aspects with practical advice on building real world NLP systems. It is evident that the authors have first hand experience in building scalable machine learning systems and have thoroughly addressed many of these challenges in the book. The one issue I did find while reading this book is that the images were not sharp enough and I had sometimes squint to read what was written. All-in-all I think this is book does a terrific job at breaking down complex technical aspects of NLP and is also a very handy reference book for experts to jog their memory about key concepts. I also found it extremely useful for interviews since a lot of the concepts are laid out in a very easy to understand fashion.

... the word "practical" in the title might make you think about the word "applied", as in code. That was what I was expecting. This is a great book to give a manager to read about NLP. They will understand things at a high level after reading it. However it just does not get the job done for someone like myself looking to apply this knowledge.

I read quite a bit but it's not often that I read a book "front-to-back" (ie., the entire book). The books that I read "front-to-back" have something truly amazing about them. This book fits that category. A little bit about myself: I have lived in Silicon Valley for over 30 years and I currently work as a Sales Engineer in the enterprise applications software space focusing on the supply chain/CRM sector. My background is 95% functional/business and 5% technical. I have read over 20 textbooks and/or technical books on AI/ML, including many of the ones recommended by AI authors (e.g., Intro to Machine Learning by Alpaydin, Deep Learning by Goodfellow/Bengio etc, Statistical Learning by Witten/Hastie etc, Hands-on Machine Learning with Scikit, Keras and Tensorflow by Geron, Reinforcement Learning by Sutton etc ... ). I love the ones mentioned in the parentheses AND yes I read almost 100% of most of them, often re-reading sections from all. This book is one of those amazing books. It gave me a great education on many aspects of NLP, and a lot more depth than I had encountered anywhere else. I don't have enough time to write an exhaustive review of all the wonderful aspects about the book, but here is a short list of new things I learned and loved learning about ... 1. 4 aspects to a language (phonemes, morphemes and lexemes, syntax, context) 2. NLP tasks 3. NLP applications 4. NLP use cases in industry My only complaint is that I wish the graphics were in color. I recommend this book to anyone interested in learning about NLP ( ... as long as they have some background training in AI/ML concepts). Excellent book ...

This is the first time I got a book which cover is correct, but the content is completely wrong. How is it possible? The book I got was "Infrastructure as Code", see Photo., but the cover says Practical Natural Language Processing. Usually, I wouldn't give a bad review, but I read that many users complained about the book's quality. Well, I wouldn't suggest to buy the book until they fix their quality issues.

Expensive book and well damaged on arrival

Great content

Denso e confuso

This is a book that was much needed in the current times. The book is essentially dividrd in the following order: a) text representation in a way comprehensive to a machine; b) the most common NLP tasks such as classification, entity recognition, knowledge dissemination; c) the above and beyond that is the tasks which involve cross engineering expertise such as social media mining etc. The authors also offers some new gems like explicable AI where they show how-to explain the decision of a classifier; working with limited data. This book is definitely going to help a lot of practitioners and the book definitely covers a 360 degree view of the NLP spectrum.