This article is part of “AI education”, a series of posts that review and explore educational content on data science and machine learning. (In partnership with Paperspace)

By many accounts, linguistics is one of the most complicated functions of the human mind. Likewise, natural language processing (NLP) is one of the most complicated subfields of artificial intelligence. Most books on AI, including educational books on machine learning, provide an introduction to natural language processing. But the field of NLP is so vast that covering all its aspects would require several separate books.

When I picked up Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems, what I expected was a book that covered Python machine learning for NLP in depth. Though the book didn’t exactly turn out to be what I had in mind, it provided the exact kind of coverage that the field misses in the craze and hype that surrounds deep learning today.

The best way to describe Practical Natural Language Processing is a zoomed-out view of the NLP landscape, a close-up of the NLP process, and plenty of practical tips and guidelines to avoid making mistakes in one of the most important fields of AI.

Two types of audience

What you take away from Practical Natural Language Processing depends on two things: Your previous background in mathematics and Python machine learning, and your involvement in the field. I recommend this book to two types of readers:

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