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You're reading from Python Machine Learning, Second Edition - Second Edition

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Pages 622 pages

Edition 2nd Edition

Languages

Python

Concepts

Machine Learning

Authors (2):

Vahid Mirjalili

Dr. Sebastian Raschka

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Table of Contents (24) Chapters

Python Machine Learning Second Edition

Credits

About the Authors

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Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Exploring the Housing dataset

Before we implement our first linear regression model, we will introduce a new dataset, the Housing dataset, which contains information about houses in the suburbs of Boston collected by D. Harrison and D.L. Rubinfeld in 1978. The Housing dataset has been made freely available and is included in the code bundle of this book. The dataset has been recently removed from the UCI Machine Learning Repository but is available online at https://raw.githubusercontent.com/rasbt/python-machine-learning-book-2nd-edition/master/code/ch10/housing.data.txt. As with each new dataset, it is always helpful to explore the data through a simple visualization, to get a better feeling of what we are working with.

Loading the Housing dataset into a data frame

In this section, we will load the Housing dataset using the pandas read_csv function, which is fast and versatile—a recommended tool for working with tabular data stored in a plaintext format.

The features of the 506 samples in...