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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 FREE CHAPTER

2. Simple Linear Regression

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

Feature Extraction

The examples discussed in the previous chapters used real-valued explanatory variables, such as the diameter of a pizza. Many machine learning problems require learning from categorical variables, text, or images. In this chapter, we will learn to create features that represent such variables.

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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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