Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Add to basketPaperback. Condition: Brand New. 422 pages. 9.61x6.69x0.89 inches. In Stock.
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
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Published by Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Published by Cambridge University Press Nov 2023, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware - Based 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 Not Elektronisches Buch 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.
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Add to basketCondition: New. Über den AutorrnrnBastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses a.
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Add to basketpaperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2019-05-01 Pages: 299 Publisher: Mechanical Industry Press This book not only introduces the basic concepts of large-scale machine learning. but also includes a wealth of case studies. The book is selected as *practical techniques and tools. and the theoretical details are not discussed in depth to provide large-scale machine learning methods (even unconventional methods). Whether it is a beginner. a general.
Published by Packt Publishing Limited, 2016
ISBN 10: 1785887211 ISBN 13: 9781785887215
Language: English
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Published by Packt Publishing Limited, 2016
ISBN 10: 1785887211 ISBN 13: 9781785887215
Language: English
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Published by Packt Publishing Limited, 2016
ISBN 10: 1785887211 ISBN 13: 9781785887215
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn to build powerful machine learning models quickly and deploy large-scale predictive applications Key Features Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Book Description Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. What you will learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine Who this book is for This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.