To get the most out of this book
Basic to intermediate knowledge of mathematics, probability, statistics, and calculus is required.
Basic to intermediate knowledge of Machine Learning (ML) algorithms is also required.
Decent knowledge of Python is required.
While reading the chapter, please run the code so that you can understand the flow of the application. All the codes are available on GitHub. The link is: https://github.com/jalajthanaki/Awesome_Machine_Learning_Solutions.
Links of code are specified in the chapters. Installation instructions for each application are also available on GitHub.
You need minimum 8 GB of RAM to run the applications smoothly. If you can run code on GPU, then it would great; otherwise you can use pre-trained models. You can download pre-trained models using the GitHub link or Google drive link. The links are specified in the chapters.
Download the example code files
You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
- Log in or register at http://www.packtpub.com.
- Select the SUPPORT tab.
- Click on Code Downloads & Errata.
- Enter the name of the book in the Search box and follow the on-screen 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-Solutions. 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. For example; "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."
A block of code is set as follows:
from __future__ import print_function import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
from __future__ import print_function
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
Any command-line input or output is written as follows:
# cp /usr/src/asterisk-addons/configs/cdr_mysql.conf.sample /etc/asterisk/cdr_mysql.conf
Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.