To get the most out of this book

This book will help you to build real-world machine learning solutions across the healthcare vertical using NumPy, pandas, matplotlib, scikit-learn, and so on. You need not have any prior knowledge before exploring this book. You will get well versed on how exactly machine learning is implemented to evaluate the efficiency of AI apps, and how to carry out simple-to-complex healthcare analytics tasks. This is a perfect entry point packed with practical examples to carry out a range of cognitive tasks. By the end of this book, you will have learned how to address long-standing challenges in the healthcare domain, and produce solutions for dealing with them.

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.packt.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 and 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-for-Healthcare-Analytics-Projects. 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/9781789536591_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: "We will then rename that file autism_detection."

A block of code is set as follows:

import sys
import pandas as pd
import sklearn
import keras
print 'Python: {}'.format(sys.version)
print 'Pandas: {}'.format(pd.__version__)
print 'Sklearn: {}'.format(sklearn.__version__)
print 'Keras: {}'.format(keras.__version__)

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

[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)

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

jupyter lab

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: "If we go into Files, we will see all the files that we have in the directory, as shown in the following screenshot."

Warnings or important notes appear like this.

Tips and tricks appear like this.