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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from   Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Length 384 pages
Edition 1st Edition
Languages
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Author (1):
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Tarek Amr Tarek Amr
Author Profile Icon Tarek Amr
Tarek Amr
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning FREE CHAPTER 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy
Classifying Text Using Naive Bayes
"Language is a process of free creation; its laws and principles are fixed, but the manner in which the principles of generation are used is free and infinitely varied. Even the interpretation and use of words involves a process of free creation."
– Noam Chomsky

Not all information exists in tables. From Wikipedia to social media, there are billions of written words that we would like our computers to process and extract bits of information from. The sub-field of machine learning that deals with textual data goes by names such as Text Mining and Natural Language Processing (NLP). These different names reflect the fact that the field inherits from multiple disciplines. On the one hand, we have computer science and statistics, and on the other hand, we have linguistics. I'd argue that the influence of linguistics was stronger when the field was at its infancy...

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