To get the most out of this book

To fully understand all the algorithms in this book, it's important to have a basic knowledge of linear algebra, probability theory, and calculus.

All practical examples are written in Python and use the scikit-learn machine learning framework, Natural Language Toolkit (NLTK), Crab, langdetect, Spark (PySpark), Gensim, Keras, and TensorFlow (deep learning frameworks). These are available for Linux, macOS X, and Windows, with Python 2.7 and 3.3+. When a particular framework is employed for a specific task, detailed instructions and references will be provided. All the examples from chapters 1 to 14 can be executed using Python 2.7 (while TensorFlow requires Python 3.5+); however, I highly suggest using a Python 3.5+ distribution. The most common choice for data science and machine learning is Anaconda (https://www.anaconda.com/download/), which already contains all the most important packages.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.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.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
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-Algorithms-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!

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://www.packtpub.com/sites/default/files/downloads/MachineLearningAlgorithmsSecondEdition_ColorImages.pdf.

Conventions used

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: "scikit-learn provides the SVC class, which is a very efficient implementation that can be used in most cases."

A block of code is set as follows:

from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

svc = SVC(kernel='linear')
print(cross_val_score(svc, X, Y, scoring='accuracy', cv=10).mean())
0.93191356542617032

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:

Warnings or important notes appear like this.

Tips and tricks appear like this.