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Machine Learning Algorithms

You're reading from Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Processing

Concepts

Data Science

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (16) Chapters

Preface

1. A Gentle Introduction to Machine Learning FREE CHAPTER

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

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 of up 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', u'cotton', u'cotton-oil', u'cpi', u'cpu', u'crude', u'dfl', u'dlr', u'dmk', u'earn', u'fuel', u'gas', u'gnp', u'gold', u'grain', u'groundnut', u'groundnut-oil', u'heat...

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