We recommend that you go through any good Python programming book or online tutorials or videos, if you are a beginner in Python. You can also have a look at DataCamp (http://www.datacamp.com) to learn Python using interactive lessons.

We also recommend that you learn some basic concepts about the Matplotlib library in Python. You can try out this tutorial for that: https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python.

You don't need to have anything installed on your system for this book before starting it. We will cover all the installation steps in the first chapter.

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/Machine-Learning-for-OpenCV-Second-Edition. 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 at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789536300_ColorImages.pdf.

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

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We can set both max_samples<1.0 and max_features<1.0 to implement the random patches method."

A block of code is set as follows:

In [1]: from sklearn.ensemble import BaggingClassifier
... from sklearn.neighbors import KNeighborsClassifier
... bag_knn = BaggingClassifier(KNeighborsClassifier(),
... n_estimators=10)

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

$ conda install package_name

Bold: Indicates a new term, an important word, or words that you see onscreen.