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LEADER 00000uam a2200409 a 4500
006 m u
007 cr cn
008 160831s2016 xx o eng
024 8 9781785889950
041 0 eng
100 1 Squire, Megan,|eauthor.
245 10 Mastering Data Mining with Python – Find patterns hidden
in your data|h[electronic resource] /|cSquire, Megan.
250 1st edition
264 1 |bPackt Publishing,|c2016.
300 268 p.
338 online resource|bcr|2rdacarrier
347 text file
520 Learn how to create more powerful data mining applications
with this comprehensive Python guide to advance data
analytics techniques About This Book Dive deeper into data
mining with Python – don’t be complacent, sharpen your
skills! From the most common elements of data mining to
cutting-edge techniques, we’ve got you covered for any
data-related challenge Become a more fluent and confident
Python data-analyst, in full control of its extensive
range of libraries Who This Book Is For This book is for
data scientists who are already familiar with some basic
data mining techniques such as SQL and machine learning,
and who are comfortable with Python. If you are ready to
learn some more advanced techniques in data mining in
order to become a data mining expert, this is the book for
you! What You Will Learn Explore techniques for finding
frequent itemsets and association rules in large data sets
Learn identification methods for entity matches across
many different types of data Identify the basics of
network mining and how to apply it to real-world data sets
Discover methods for detecting the sentiment of text and
for locating named entities in text Observe multiple
techniques for automatically extracting summaries and
generating topic models for text See how to use data
mining to fix data anomalies and how to use machine
learning to identify outliers in a data set In Detail Data
mining is an integral part of the data science pipeline.
It is the foundation of any successful data-driven
strategy – without it, you'll never be able to uncover
truly transformative insights. Since data is vital to just
about every modern organization, it is worth taking the
next step to unlock even greater value and more meaningful
understanding. If you already know the fundamentals of
data mining with Python, you are now ready to experiment
with more interesting, advanced data analytics techniques
using Python's easy-to-use interface and extensive range
of libraries. In this book, you'll go deeper into many
often overlooked areas of data mining, including
association rule mining, entity matching, network mining,
sentiment analysis, named entity recognition, text
summarization, topic modeling, and anomaly detection. For
each data mining technique, we'll review the state-of-the-
art and current best practices before comparing a wide
variety of strategies for solving each problem. We will
then implement example solutions using real-world data
from the domain of software e...
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538 Mode of access: World Wide Web.
542 |fCopyright © 2016 Packt Publishing
550 Made available through: Safari, an O’Reilly Media Company.
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