Layton - Learning data mining with Python: use Python to manipulate data and build predictive models

BIRMINGHAM - MUMBAI

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First published: July 2015
Second edition: April 2017

Production reference: 1250417

ISBN 978-1-78712-678-7

www.packtpub.com

Robert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline (www.datapipeline.com.au). Next, he created Eureaktive (www.eureaktive.com.au), which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs www.learningtensorflow.com, which is one of the world's premier tutorial websites for Google's TensorFlow library.

Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations.

Robert can be best reached via Twitter @robertlayton

Asad Ahamad is a data enthusiast and loves to work on data to solve challenging problems.

He did his masters in Industrial Mathematics with Computer Application from Jamia Millia Islamia, New Delhi. He admires Mathematics a lot and always tries to use it to gain maximum profit for business.

He has good experience working on data mining, machine learning and data science and worked for various multinationals in India. He mainly uses R and Python to perform data wrangling and modeling. He is fond of using open source tools for data analysis.

He is active social media user. Feel free to connect him on twitter @asadtaj88

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The second revision of Learning Data Mining with Python was written with the programmer in mind. It aims to introduce data mining to a wide range of programmers, as I feel that this is critically important to all those in the computer science field. Data mining is quickly becoming the building block of the next generation of Artificial Intelligence systems. Even if you don't find yourself building these systems, you will be using them, interfacing with them, and being guided by them. Understand the process behind them is important and helps you get the best out of them.
The second revision builds upon the first. Many of chapters and exercises are similar, although new concepts are introduced and exercises are expanded in scope. Those that had read the first revision should be able to move quickly through the book and pick up new knowledge along the way and engage with the extra activities proposed. Those new to the book are encouraged to take their time, do the exercises and experiment. Feel free to break the code to understand it, and reach out if you have any questions.
As this is a book aimed at programmers, we assume that you have some knowledge of programming and of Python itself. For this reason, there is little explanation of what the Python