Ebook Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
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Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
Ebook Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
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Product details
Paperback: 332 pages
Publisher: O'Reilly Media; 1 edition (July 1, 2018)
Language: English
ISBN-10: 9781491963043
ISBN-13: 978-1491963043
ASIN: 1491963042
Product Dimensions:
7 x 0.6 x 9 inches
Shipping Weight: 7 ounces (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#46,101 in Books (See Top 100 in Books)
In a hot summer evening when I did book browsing at Barnes and Noble, I found this book and immediately liked it. Due to the relative large price difference, I did not bought it at the bookstore but ordered a copy from Amazon. I am a mathematician and semi-physicist by training and data scientist by vocation, and I never enjoy reading technical books except when I have to for work-related studies (I enjoy theoretical books such as Lectures on Quantum Mechanics or Group Theory in a Nutshell for Physicists or etc.) But let me say I do enjoy reading this book. This book gives a very good introduction to fundamental concepts of natural language processing (NLP) as well as a survey of current NLP landscape (formulations, NLP libraries, applications, etc.). It is also hands-on. Good books of such kind should never be too long (A long book usu. = collection of stuff you can find by Googling). I am glad this book is not too long (300-ish pages), so you can read end to end and not miss every single idea of the authors'. What is particularly appreciated (from my perspective) is that the book has several chapters on the cutting edge NLP methodology (e.g. knowledge graph approach to NLP, chatbot design princples, etc.) Thanks for the good work of the authors!
I’d put myself more in the realm of domain specialist with an interest of how text mining tools could be practically leveraged. The alphabet soup of frameworks and models to text minimum can be confounding.... as unstructured text tends to be for us looking for clearer insights.The authors do an great job of taking you through key concepts and applications, all anchored in Python code (and later some examples from Spark environments). While the code does require some attention to work through if you are not primarily a programmer, they are worth pondering over.To get the full value out of the book, its most worth working live through some of the sample code and libraries. Many of you (as I was) probably will not be able to do this in the first run. But I certainly see this as a resource book I will come back to, once I get more deeply into certain domain applications.If anything, this is helpful to make sense of bridging some of the Babalesque that can happen when data science teams gear up for applied text analysis.
Exactly what I needed. Thank you.
I was disappointed that this book isn't available for Kindle Cloud Reader (in my browser). I often open books on my machine at work to work through the code examples from technical books I'm reading. This limitation means I have to install a Kindle reader app for every machine I want to access the book. Bummer.
It is a very easy read with invaluable insights to basic to intermediate NLP concepts and code snippets. I really liked the fact that a number of relevant libraries were discussed, such as spacy, gensim and the good ol' nltk. Note that this is not an advanced book, but light years ahead of most tutorials or 'recipe' book you see everyday.
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