To get the most out of this book

Ideally, you should have a basic knowledge of how Python code is written and structured. If you are not familiar with Python or are not clear how programming languages work in general, then please take at look at a book on Python. A book dealing with Python from a data science perspective would be ideal for you.

If you already have a basic idea of NLP and machine learning in general, this book should be easy for you to grasp. If you are starting out in NLP, that should not be too much of an issue if you are willing to dive deep into the mathematics covered. I have taken care to explain the mathematical concepts covered in this book, but if this too seems too difficult, please write to us and let us know.

A willingness on the part of the reader to dive deep and try out all the code is assumed.

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/fastText-Quick-Start-Guide. 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!

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: "Commands such as cat, grep, sed, and awk are quite old and their behavior is well-documented on the internet."

A block of code is set as follows:

import csv
import sys
w = csv.writer(sys.stdout)
for row in csv.DictReader(sys.stdin):
 w.writerow([row['stars'], row['text'].replace('\n', '')])

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import csv
import sys
w = csv.writer(sys.stdout)
for row in csv.DictReader(sys.stdin):
    w.writerow([row['stars'], row['text'].replace('\n', '')])

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

$ cat data/yelp/yelp_review.csv | \
  python parse_yelp_dataset.py \
 > data/yelp/yelp_review.v1.csv

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.