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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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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

NLTK and built-in corpora

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 screenshot:

NLTK downloader window

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. Alternatively, it's possible to install all dependencies using the following command:

python -m nltk.downloader...
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