Using Python for machine learning
Python is one of the most popular programming languages for data science and thanks to its very active developer and open source community, a large number of useful libraries for scientific computing and machine learning have been developed.
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 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. In the later chapters, when we focus on a subfield of machine learning called deep learning, we will use the latest version of the TensorFlow library, which specializes in training so-called deep neural network models very efficiently by utilizing graphics cards.
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 3.7 or higher, and it is recommended that you use the most recent version of Python 3 that is currently available. Some of the code may also be compatible with Python 2.7, but as the official support for Python 2.7 ends in 2019, and the majority of open source libraries have already stopped supporting Python 2.7 (https://python3statement.org), we strongly advise that you use Python 3.7 or newer.
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 commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering into one user-friendly, cross-platform distribution. The Anaconda installer can be downloaded at https://docs.anaconda.com/anaconda/install/, and an Anaconda quick start guide is available at https://docs.anaconda.com/anaconda/user-guide/getting-started/.
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 your learning experience and visualize quantitative data, which is often extremely useful to make sense of it, we will use the very customizable Matplotlib library.
The version numbers of the major Python packages that were used to write 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, these version numbers to ensure that the code examples run correctly:
- NumPy 1.17.4
- SciPy 1.3.1
- scikit-learn 0.22.0
- Matplotlib 3.1.0
- pandas 0.25.3
