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You're reading from  Python Data Mining Quick Start Guide

Product typeBook
Published inApr 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789800265
Edition1st Edition
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Author (1)
Nathan Greeneltch
Nathan Greeneltch
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Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
Read more about Nathan Greeneltch

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What will and will not be covered in this book

A quick and dirty description of data mining I hear in the field can be paraphrased as: "Descriptive and predictive analytics with a focus on previously hidden relationships or trends". As such, this book will cover these topics and skip the predictive analytics that focus on automation of obvious prediction, along with the entire field of prescriptive analytics entirely. This text is meant to be a quick start guide, so even the relevant fields of study will only be skimmed over and summarized. Please see the Recommended reading for further explanation section for inquiring minds that want to delve deeper into some of the subjects covered in this book.

Preprocessing and data transformation are typically considered to be outside of the data mining category. One of the goals of this book is to provide full working data mining examples, and basic preprocessing is required to do this right. So, this book will cover those topics, before delving in to the more traditional mining strategies.

Throughout this book, I will throw in tips I've learned along my career journey around how to apply data mining to solve real-world problems. I will denote them in a special tip box like this one.

Recommended readings for further explanation

These books are good for more in-depth discussions and as an introduction to important and relevant topics. I recommend that you start with these if you want to become an expert:

  • Data mining in practice:

Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition by Ian H. Witten (author), Eibe Frank (author), Mark A. Hall (author), Christopher J. Pal

  • Data mining advanced discussion and mathematical foundation:

Data Mining and Analysis: Fundamental Concepts and Algorithms, 1st Edition by Mohammed J. Zaki (author), Wagner Meira Jr (author)

  • Computer science taught with Python:

Python Programming: An Introduction to Computer Science, 3rd Edition by John Zelle (author)

  • Python machine learning and analytics:

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition Paperback—September 20, 2017 by Sebastian Raschka (author), Vahid Mirjalili (author)

Advanced Machine Learning with Python Paperback—July 28, 2016 by John Hearty

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Published in: Apr 2019Publisher: PacktISBN-13: 9781789800265
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Author (1)

author image
Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
Read more about Nathan Greeneltch