To get the most out of this book

You will need Python 3.x installed on your computer. It is a good practice to set up a virtual environment to install the required libraries into. It's up to you whether you wish to use Python's venv module, the virtual environment provided by Anaconda, or any other option you like. I'll be using pip to install the libraries needed in the book, but once more, it is up to you whether you prefer to use conda or any other alternatives.

In Chapter 1, Introduction to Machine Learning, I will explain the essential libraries you need to install to get started. I will show you how to install them using the same versions tested here, so we are both on the same page throughout the rest of this book. Whenever we need to install any additional libraries in the later chapters, I will also explain how to set them up.

I used Jupyter Notebooks to run the code in this book and display the accompanying graphs. I recommend that you also go to the Project Jupyter site and install Jupyter Notebook or Jupyter Lab. This setup is usually recommended when running experimental code. It helps you cut your code into pieces, iterate on each part separately, and display the resulting graphs alongside the code. When it comes to writing production code, then you may use your favorite integrated development environment (IDE) instead.

In addition to the software needed, you will sometimes need to download additional datasets. I will provide links to the required datasets when needed, and give step-by-step explanations on how to download and preprocess them.

I wrote the entire book and ran its code on a MacBook Pro with 16 GB RAM. I expect the code here to run on any other operating system, whether it is Microsoft Windows or any one of the different Linux distributions. It is more common for machine learning algorithms to hit a memory limitation before hitting a CPU bottleneck. Nevertheless, for most of the code and the datasets used here, I would expect computers with less memory than mine to still work fine.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-scikit-learn-and-Scientific-Python-Toolkits. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available athttps://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here:https://static.packt-cdn.com/downloads/9781838826048_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText:Indicates code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles.Here is an example:"We are going to use its fit_transform variable and a transform method."

A block of code is set as follows:

import numpy as np
import scipy as sp
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Any command-line input or output is written as follows:

          $ pip install jupyter
          

          $ pip install matplotlib
        

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "One-hot encoding is recommended for linear models and K-Nearest Neighbor (KNN) algorithms."

Warnings or important notes appear like this.

Tips and tricks appear like this.