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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example Implement machine learning algorithms and techniques to build intelligent systems

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789616729
Length 382 pages
Edition 2nd Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning FREE CHAPTER
2. Getting Started with Machine Learning and Python 3. Section 2: Practical Python Machine Learning By Example
4. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques 5. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms 6. Detecting Spam Email with Naive Bayes 7. Classifying Newsgroup Topics with Support Vector Machines 8. Predicting Online Ad Click-Through with Tree-Based Algorithms 9. Predicting Online Ad Click-Through with Logistic Regression 10. Scaling Up Prediction to Terabyte Click Logs 11. Stock Price Prediction with Regression Algorithms 12. Section 3: Python Machine Learning Best Practices
13. Machine Learning Best Practices 14. Other Books You May Enjoy

How computers understand language - NLP

In Chapter 1, Getting Started with Machine Learning and Python, it was mentioned that machine learning driven programs or computers are good at discovering event patterns by processing and working with data. When the data is well structured or well defined, such as in a Microsoft Excel spreadsheet table and relational database table, it is intuitively obvious why machine learning is better at dealing with it than humans. Computers read such data the same way as humans, for example, revenue: 5,000,000 as the revenue being 5 million and age: 30 as age being 30; then computers crunch assorted data and generate insights. However, when the data is unstructured, such as words with which humans communicate, news articles, or someone's speech in French, it seems computers cannot understand words as well as human do (yet).

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