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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example The easiest way to get into machine learning

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781783553112
Length 254 pages
Edition 1st Edition
Languages
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Authors (2):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (9) Chapters Close

Preface 1. Getting Started with Python and Machine Learning FREE CHAPTER 2. Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms 3. Spam Email Detection with Naive Bayes 4. News Topic Classification with Support Vector Machine 5. Click-Through Prediction with Tree-Based Algorithms 6. Click-Through Prediction with Logistic Regression 7. Stock Price Prediction with Regression Algorithms 8. Best Practices

Getting the data

It is possible to download the data manually from the original website or many online repositories. However, there are also many versions of the dataset--some are cleaned in a certain way and some in the raw form. To avoid confusion, it is best to use a consistent acquisition method. The scikit-learn library provides a utility function of loading the dataset.Once the dataset is downloaded, it is automatically cached. We won’t need to download the same dataset twice. In most cases, caching the dataset, especially for a relatively small one, is considered a good practice. Other Python libraries also support download utilities, but not all of them implement automatic caching. This is another reason why we love scikit-learn.

To load the data, we can import the loader function for the 20 newsgroups data as follows:

>>> from sklearn.datasets import fetch_20newsgroups  

Then we can download...

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