Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press Nov 2023, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: 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.
Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
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ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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ISBN 10: 100931825X ISBN 13: 9781009318259
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ISBN 10: 100931825X ISBN 13: 9781009318259
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ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
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Verlag: Cambridge University Press, GB, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. 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 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.
Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, GB, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. 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 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.
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In den WarenkorbPaperback. Zustand: Brand New. 422 pages. 9.61x6.69x0.89 inches. In Stock.
Verlag: Cambridge University Press, Cambridge, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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 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. A hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Verlag: CAMBRIDGE UNIVERSITY PRESS, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: CAMBRIDGE UNIVERSITY PRESS, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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Verlag: Cambridge University Press, Cambridge, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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 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. A hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: Cambridge University Press, Cambridge, 2023
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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 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. A hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Verlag: Cambridge University Press, 2024
ISBN 10: 100931825X ISBN 13: 9781009318259
Sprache: Englisch
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