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Multivariate Statistics and Machine Learning

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Hardback
584 Pages
28 December 2025
RRP: $362.00
$357.00
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Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.

The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued.

With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.

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    Product Details
    ISBN-13
    9781032454276
    Author
    Daniel J. Denis
    Publisher
    Taylor & Francis
    Published
    28 December 2025
    Format
    Hardback
    Pages
    584

    This product hasn't received any reviews yet. Be the first to review this product!

    RRP: $362.00
    $357.00
    Ships in 2-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Multivariate Statistics and Machine Learning

    RRP: $362.00
    $357.00

    Description

    Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.

    The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued.

    With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.

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