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Mastering Machine Learning with scikit-learn

You're reading from Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn

Product type Paperback

Published in Jul 2017

Publisher

ISBN-13 9781788299879

Length 254 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Gavin Hackeling

View More author details

Table of Contents (15) Chapters

Preface

1. The Fundamentals of Machine Learning

2. Simple Linear Regression FREE CHAPTER

3. Classification and Regression with k-Nearest Neighbors

4. Feature Extraction

5. From Simple Linear Regression to Multiple Linear Regression

6. From Linear Regression to Logistic Regression

7. Naive Bayes

8. Nonlinear Classification and Regression with Decision Trees

9. From Decision Trees to Random Forests and Other Ensemble Methods

10. The Perceptron

11. From the Perceptron to Support Vector Machines

12. From the Perceptron to Artificial Neural Networks

13. K-means

14. Dimensionality Reduction with Principal Component Analysis

Summary

In this chapter, we discussed the SVM, a powerful model that can be used for classification and regression tasks. SVMs efficiently map features to higher dimensional spaces in which classes may be linearly separable. SVMs also maximize the margin between the decision boundary and the nearest training instances. In the next chapter, we will discuss models called ANN; like SVMs, they extend the perceptron to overcome its limitations.

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Authors (1)

Gavin Hackeling

Gavin Hackeling

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.

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