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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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785889622
Length 360 pages
Edition 1st 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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Table of Contents (16) 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. 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

NLTK and built-in corpora

Natural Language Toolkit (NLTK) is a very powerful Python framework that implements most NLP algorithms and will be adopted in this chapter together with scikit-learn. Moreover, NLTK provides some built-in corpora that can be used to test algorithms. Before starting to work with NLTK, it's normally necessary to download all the additional elements (corpora, dictionaries, and so on) using a specific graphical interface. This can be done in the following way:

import nltk

>>> nltk.download()

This command will launch the user interface, as shown in the following figure:

It's possible to select every single feature or download all elements (I suggest this option if you have enough free space) to immediately exploit all NLTK functionalities.

NLTK can be installed using pip (pip install -U nltk) or with one of the binary distributions...
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