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9783030185473 - Python for Probability, Statistics, and Machine Learning by Unpingco, José (21 results)
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Python for Probability, Statistics, and Machine Learning
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
paperback. Condition: Very Good. Cover and edges may have some wear.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: Very Good. Very Good Condition. Five star seller - Buy with confidence!
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Python for Probability, Statistics, and Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 60.59
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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PF. Condition: New.
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Python for Probability, Statistics, and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Python For Probability, Statistics, And
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Python for Probability, Statistics, and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. pp. 384.
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Paperback. Condition: Brand New. 2nd edition. 400 pages. 9.25x6.10x0.94 inches. In Stock.
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Condition: New.
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Python for Probability, Statistics, and Machine Learning
Language: English
Published by Springer International Publishing, 2020
ISBN 10: 3030185478 ISBN 13: 9783030185473
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated \*Programming Tips\* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
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Taschenbuch. Condition: 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.
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Paperback. Condition: New. New. book.
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Condition: new. Questo è un articolo print on demand.
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Python for Probability, Statistics, and Machine Learning
Language: English
Published by Springer International Publishing Aug 2020, 2020
ISBN 10: 3030185478 ISBN 13: 9783030185473
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated \*Programming Tips\* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. 400 pp. Englisch.
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Condition: New. Print on Demand pp. 384.
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Condition: New. PRINT ON DEMAND pp. 384.
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Python for Probability, Statistics, and Machine Learning
Language: English
Published by Springer International Publishing, 2020
ISBN 10: 3030185478 ISBN 13: 9783030185473
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Features fully updated explanation on how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methodsNew edition features Python version 3.7 and connects to key open-source Python communities and corre.
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Python for Probability, Statistics, and Machine Learning
Language: English
Published by Springer, Palgrave Macmillan Aug 2020, 2020
ISBN 10: 3030185478 ISBN 13: 9783030185473
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 400 pp. Englisch.
