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Hands-on NLP with NLTK and Scikit-learn
Hands-on NLP with NLTK and Scikit-learn

Hands-on NLP with NLTK and Scikit-learn: Natural Language Processing with Python tools

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Profile Icon James Cross
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€117.99
Video Jul 2018 2hrs 46mins 1st Edition
Video
€117.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon James Cross
Arrow right icon
€117.99
Video Jul 2018 2hrs 46mins 1st Edition
Video
€117.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€117.99
Subscription
Free Trial
Renews at €18.99p/m

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Key benefits

  • Build actual solutions backed by machine learning and Natural Language Processing models, instead of meandering in theory and mathematical symbols.
  • Learn the fundamentals of Natural Language Processing by acquiring open-source data, processing text with NLTK in Python, and predicting language classes with scikit-learn.

Description

There is an overflow of text data online nowadays. As a Python developer, you need to create a new solution using Natural Language Processing for your next project. Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do? Hands-on NLP with NLTK and scikit-learn is the answer. This course puts you right on the spot, starting off with building a spam classifier in our first video. At the end of the course, you are going to walk away with three NLP applications: a spam filter, a topic classifier, and a sentiment analyzer. There is no need for fancy mathematical theory, just plain English explanations of core NLP concepts and how to apply those using Python libraries. Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease. All the code and supporting files are available on GitHub at: https://github.com/PacktPublishing/Hands-on-NLP-with-NLTK-and-scikit-learn- This course uses Python 3.6, TensorFlow 1.4, NLTK 2, and scikit-learn 0.19, while not the latest version available, it provides relevant and informative content for legacy users of NLP with NLTK and Scikit-learn.

Who is this book for?

This course is for developers, data scientists, and programmers who want to learn about practical Natural Language Processing with Python in a hands-on way. Developers who have an upcoming project that needs NLP, or a pile of unstructured text data on their hands, and don't know what to do with it, will find this course useful. Prior programming experience with Python is assumed along with being comfortable dealing with machine learning terms such as supervised learning, regression, and classification. No prior Natural Language Processing or text mining experience is needed.

What you will learn

  • Build end-to-end Natural Language Processing solutions, ranging from getting data for your model to presenting its results.
  • Core NLP concepts such as tokenization, stemming, and stop word removal.
  • Use open source libraries such as NLTK, scikit-learn, and spaCy to perform routine NLP tasks.
  • Classify emails as spam or not-spam using basic NLP techniques and simple machine learning models.
  • Put documents in their relevant topics using techniques such as TF-IDF, SVMs, and LDAs.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 25, 2018
Length: 2hrs 46mins
Edition : 1st
Language : English
ISBN-13 : 9781789345612
Category :
Languages :
Tools :

What do you get with a video?

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Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details

Publication date : Jul 25, 2018
Length: 2hrs 46mins
Edition : 1st
Language : English
ISBN-13 : 9781789345612
Category :
Languages :
Tools :

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€117.99
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Table of Contents

6 Chapters
Working with Natural Language Data Chevron down icon Chevron up icon
Spam Classification with an Email Dataset Chevron down icon Chevron up icon
Sentiment Analysis with a Movie Review Dataset Chevron down icon Chevron up icon
Boosting the Performance of Your Models with N-grams Chevron down icon Chevron up icon
Document Classification with a Newsgroup Dataset Chevron down icon Chevron up icon
Advanced Topic Modelling with TF-IDF, LSA, and SVMs Chevron down icon Chevron up icon
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