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DATA MINING: PRACTICL MACHINE LEARNING TOOLS AND TECH 4

Computer Science

Mathematics & Science

Gt Product Group

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B2B GT

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B2B & B2C

<P>This book offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.</P>

Data Mining

COLFR

COLSO

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Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Table of Contents.
List of Figures.
List of Tables.
Preface.
Introduction to Data Mining.
1: What&#x2019;s it All About?.
2: Input: Concepts, Instances, Attributes.
3: Output: Knowledge Representation.
4: Algorithms: The Basic Methods.
5: Credibility: Evaluating What&#x2019;s Been Learned.
More Advanced Machine Learning Schemes.
6: Trees and Rules.
7: Extending Instance-Based and Linear Models.
8: Data Transformations.
9: Probabilistic Methods.
10: Deep Learning.
11: Beyond Supervised and Unsupervised Learning.
12: Ensemble Learning.
13: Moving on Applications and Beyond.
Appendix A: Theoretical Foundations.
Appendix B: The WEKA Workbench.
References.
Index.

Data Mining.css-1p8rvzy{position:absolute;padding-left:8px;}

Data Mining