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  • 11
    Monograph available for loan
    Monograph available for loan
    Chicester : Wiley-Blackwell
    Call number: 18/M 15.0287
    Description / Table of Contents: Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization.In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. * Predict outcomes using linear and ensemble algorithm families * Build predictive models that solve a range of simple and complex problems * Apply core machine learning algorithms using Python * Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.
    Type of Medium: Monograph available for loan
    Pages: XXIX, 326 S. : Ill., graph. Darst.
    ISBN: 9781118961742
    Classification:
    Informatics
    Location: Reading room
    Branch Library: GFZ Library
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  • 12
    Call number: 9/M 07.0421(401)
    In: Geological Society special publication
    Type of Medium: Monograph available for loan
    Pages: vi, 448 S.
    ISBN: 9781862396326
    Series Statement: Geological Society special publication 401
    Classification:
    Tectonics
    Location: Reading room
    Branch Library: GFZ Library
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  • 13
    Monograph available for loan
    Monograph available for loan
    Cambridge, Mass. [u.a.] : MIT Press
    Call number: M 15.0095
    Description / Table of Contents: "Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships.After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
    Type of Medium: Monograph available for loan
    Pages: xvi, 383 S. , Ill.
    ISBN: 9780262028561
    Classification:
    Informatics
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 14
    Call number: 9/M 07.0421(399)
    In: Geological Society special publication
    Type of Medium: Monograph available for loan
    Pages: 457 S.
    ISBN: 9781862396531
    Series Statement: Geological Society special publication 399
    Classification:
    Tectonics
    Location: Reading room
    Branch Library: GFZ Library
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