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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

Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms

We went through a bunch of fundamental machine learning concepts in the last chapter. We learned them along with analogies the fun way, such as studying for the exams, designing driving schedule, and so on. As promised, starting from this chapter as the second step of our learning journal, we will be discovering in detail several import machine learning algorithms and techniques. Beyond analogies, we will be exposed to and will solve real-world examples, which makes our journal more interesting. We start with a classic natural language processing problem--newsgroups topic modeling in this chapter. We will gain hands-on experience in working with text data, especially how to convert words and phrases into machine-readable values. We will be tackling the project in an unsupervised learning manner, using clustering algorithms, including k-means clustering...

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