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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms Popular algorithms for data science and machine learning

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789347999
Length 522 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Toc

Table of Contents (19) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Regression Algorithms 5. Linear Classification Algorithms 6. Naive Bayes and Discriminant Analysis 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Advanced Clustering 11. Hierarchical Clustering 12. Introducing Recommendation Systems 13. Introducing Natural Language Processing 14. Topic Modeling and Sentiment Analysis in NLP 15. Introducing Neural Networks 16. Advanced Deep Learning Models 17. Creating a Machine Learning Architecture 18. Other Books You May Enjoy

A sample text classifier based on the Reuters corpus

We are going to build a sample text classifier based on the NLTK Reuters corpus. This one is made up of thousands of news lines divided into 90 categories:

from nltk.corpus import reuters

print(reuters.categories())
[u'acq', u'alum', u'barley', u'bop', u'carcass', u'castor-oil', u'cocoa', u'coconut', u'coconut-oil', u'coffee', u'copper', u'copra-cake', u'corn', ...

To simplify the process, we'll take only two categories, which have a similar number of documents:

import numpy as np

Xr = np.array(reuters.sents(categories=['rubber']))
Xc = np.array(reuters.sents(categories=['cotton']))
Xw = np.concatenate((Xr, Xc))

As each document is already split into tokens and we want to apply...

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