You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER

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

Using Python for machine learning

Python is one of the most popular programming languages for data science and therefore enjoys a large number of useful add-on libraries developed by its great developer and open-source community.

Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast and vectorized operations on multidimensional arrays.

For machine learning programming tasks, we will mostly refer to the scikit-learn library, which is currently one of the most popular and accessible open source machine learning libraries.

Installing Python and packages from the Python Package Index

Python is available for all three major operating systems—Microsoft Windows, macOS, and Linux—and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org.

This book is written for Python version 3.5.2 or higher, and it is recommended you use the most recent version of Python 3 that is currently available, although most of the code examples may also be compatible with Python 2.7.13 or higher. If you decide to use Python 2.7 to execute the code examples, please make sure that you know about the major differences between the two Python versions. A good summary of the differences between Python 3.5 and 2.7 can be found at https://wiki.python.org/moin/Python2orPython3.

The additional packages that we will be using throughout this book can be installed via the pip installer program, which has been part of the Python standard library since Python 3.3. More information about pip can be found at https://docs.python.org/3/installing/index.html.

After we have successfully installed Python, we can execute pip from the Terminal to install additional Python packages:

pip install SomePackage

Already installed packages can be updated via the --upgrade flag:

pip install SomePackage --upgrade

Using the Anaconda Python distribution and package manager

A highly recommended alternative Python distribution for scientific computing is Anaconda by Continuum Analytics. Anaconda is a free—including for commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering in one user-friendly cross-platform distribution. The Anaconda installer can be downloaded at http://continuum.io/downloads, and an Anaconda quick-start guide is available at https://conda.io/docs/test-drive.html.

After successfully installing Anaconda, we can install new Python packages using the following command:

conda install SomePackage

Existing packages can be updated using the following command:

conda update SomePackage

Packages for scientific computing, data science, and machine learning

Throughout this book, we will mainly use NumPy's multidimensional arrays to store and manipulate data. Occasionally, we will make use of pandas, which is a library built on top of NumPy that provides additional higher-level data manipulation tools that make working with tabular data even more convenient. To augment our learning experience and visualize quantitative data, which is often extremely useful to intuitively make sense of it, we will use the very customizable Matplotlib library.

The version numbers of the major Python packages that were used for writing this book are mentioned in the following list. Please make sure that the version numbers of your installed packages are equal to, or greater than, those version numbers to ensure the code examples run correctly: