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Machine Learning Algorithms

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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781789347999

Length 522 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (19) Chapters

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

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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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