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Añadir al carrito2nd Ed. XIV, 384 p. Hardcover. 2nd Ed. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
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Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2023
ISBN 10: 3031046501 ISBN 13: 9783031046506
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
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Añadir al carritoPaperback. Condición: new. Paperback. Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2022
ISBN 10: 3031046471 ISBN 13: 9783031046476
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 102,30
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Añadir al carritoHardcover. Condición: new. Hardcover. Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 3rd edition. 526 pages. 9.26x6.10x1.06 inches. In Stock.
Idioma: Inglés
Publicado por Springer International Publishing AG, CH, 2022
ISBN 10: 3031046471 ISBN 13: 9783031046476
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoHardback. Condición: New. Third Edition 2022.
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Añadir al carritoTaschenbuch. Condición: Neu. Python for Probability, Statistics, and Machine Learning | José Unpingco | Taschenbuch | xiv | Englisch | 2020 | Springer | EAN 9783030185473 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Springer International Publishing, 2023
ISBN 10: 3031046501 ISBN 13: 9783031046506
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 69,54
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with 'Programming Tips' that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers.Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.