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020 _a9781009318242
020 _z9781009318259
041 _aEnglish
082 0 4 _a006.312
_223
_bTRI
100 1 _aTriguero, Isaac,
100 1 _eauthor.
245 1 0 _aLarge-scale data analytics with Python and Spark :
_ba hands-on guide to implementing machine learning solutions /
_cIsaac Triguero, Mikel Galar.
260 _aUnited Kingdom,
_bCambridge University Press, Cambridge,
_c2024.
300 _axvi, 378 pages :
_b illustrations ;
_c25 cm
504 _aIncludes bibliographical references and index.
505 _aIntroduction MapReduce Hadoop Spark Spark SQL and DataFrames Machine Learning with Spark Machine Learning for Big Data Implementing Classical Methods: k-Means and Linear Regression Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment
506 _aAccess restricted to subscribing institutions.
520 _aBased on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
630 0 0 _aSPARK (Electronic resource)
650 0 _aData mining.
650 0 _aPython (Computer program language)
650 0 _aBig data.
700 1 _aGalar, Mikel,
700 1 _eauthor.
776 0 8 _iPrint version:
_z9781009318259.
856 4 0 _uhttps://ezproxy.lib.gla.ac.uk/login?url=https://doi.org/10.1017/9781009318242
856 4 0 _zConnect to resource
907 _a.b41271294
942 _2ddc
_cBOOKS
999 _c46262
_d46262