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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 3. A Developer's Approach to Data Cleaning 4. Data Mining and the Database Developer 5. Statistical Analysis for the Database Developer 6. Database Progression to Database Regression 7. Regularization for Database Improvement 8. Database Development and Assessment 9. Databases and Neural Networks 10. Boosting your Database 11. Database Classification using Support Vector Machines 12. Database Structures and Machine Learning

Definition and purpose of an SVM

I support Vector Machines, do you?

In the field of machine learning, SVMs are similarly recognized as support vector networks and are defined as supervised learning models with accompanying learning algorithms that analyze data used for classification.

An important note about SVMs is that they are all about the ability to successfully perform pattern recognition. In other words, SVMs promote the ability to extend patterns found in data that are:

Not linearly separable by transformations of original data to map into new space.

Again, everything you will find and come to know about SVMs will align with the idea that an SVM is a supervised machine learning algorithm which is most often used for classification or regression problems in statistics.

The...

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