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LEADER 00000uam a2200409 a 4500 
003    CaSebORM 
005    20190131015533.1 
006    m        u         
007    cr cn          
008    160831s2016    xx      o           eng   
019    SAFARI9781785889950 
020    |z9781785889950 
020    |z9781785889950 
020    |z9781785885914 
024 8  9781785889950 
035    (Safari)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. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
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... 
533    Electronic reproduction.|bBoston, MA :|cSafari,|nAvailable
       via World Wide Web. 
538    Mode of access: World Wide Web. 
542    |fCopyright © 2016 Packt Publishing 
550    Made available through: Safari, an O’Reilly Media Company.
655  7 Electronic books.|2local 
710 2  Safari, an O’Reilly Media Company. 
830  0 Safari Books Online 
856 40 |u
       &orpq&email=^u|zConnect to ebook (University of Melbourne 
990    Safari Books Online 
990    Batch Ebook load (bud2) - do not edit, delete or attach 
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