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Machine Learning Algorithms

You're reading from Machine Learning Algorithms

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Pages 360 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. A Gentle Introduction to Machine Learning

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

Summary

In this chapter, we discussed how a support vector machine works in both linear and non-linear scenarios, starting from the basic mathematical formulation. The main concept is to find the hyperplane that maximizes the distance between the classes by using a limited number of samples (called support vectors) that are closest to the separation margin.

We saw how to transform a non-linear problem using kernel functions, which allow remapping of the original space to a another high-dimensional one where the problem becomes linearly separable. We also saw how to control the number of support vectors and how to use SVMs for regression problems.

In the next chapter, we're going to introduce another classification method called decision trees, which is the last one explained in this book.

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