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Python Machine Learning - Third Edition

You're reading from Python Machine Learning - Third Edition

Product type Book

Published in Dec 2019

Publisher Packt

ISBN-13 9781789955750

Pages 772 pages

Edition 3rd Edition

Languages

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Table of Contents (21) Chapters

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 Datasets – 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

17. Generative Adversarial Networks for Synthesizing New Data

18. Reinforcement Learning for Decision Making in Complex Environments

19. Other Books You May Enjoy

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20. Index

Exploring the Housing dataset

Before we implement the first linear regression model, we will discuss 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 recently been removed from the UCI Machine Learning Repository but is available online at https://raw.githubusercontent.com/rasbt/python-machine-learning-book-3rd-edition/master/ch10/housing.data.txt or scikit-learn (https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/data/boston_house_prices.csv). 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...

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Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

See other products by Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

See other products by Vahid Mirjalili

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