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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
Published by Springer Nature Switzerland AG (edition 2nd ed. 2019), 2019
ISBN 10: 3030185443 ISBN 13: 9783030185442
Language: English
Hardcover. Condition: Good. 2nd ed. 2019. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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Python for Probability, Statistics, and Machine Learning
Published by Springer International Publishing AG, 2019
ISBN 10: 3030185443 ISBN 13: 9783030185442
Language: English
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Python for Probability, Statistics, and Machine Learning
Published by Springer International Publishing, 2023
ISBN 10: 3031046501 ISBN 13: 9783031046506
Language: English
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Python for Probability, Statistics, and Machine Learning
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Python for Probability, Statistics, and Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Python for Probability, Statistics, and Machine Learning
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Python for Probability, Statistics, and Machine Learning
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Python for Probability, Statistics, and Machine Learning
Published by Springer-Verlag GmbH, 2016
ISBN 10: 3319307150 ISBN 13: 9783319307152
Language: English
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Add to basketpaperback. Condition: Sehr gut. 400 Seiten; 9783030185473.2 Gewicht in Gramm: 1.
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Python for Probability, Statistics, and Machine Learning
Published by Springer International Publishing AG, 2022
ISBN 10: 3031046471 ISBN 13: 9783031046476
Language: English
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Python for Probability, Statistics, and Machine Learning
Published by Springer International Publishing AG, 2022
ISBN 10: 3031046471 ISBN 13: 9783031046476
Language: English
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Python for Probability, Statistics, and Machine Learning
Published by Springer International Publishing, 2020
ISBN 10: 3030185478 ISBN 13: 9783030185473
Language: English
US$ 75.92
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Add to basketTaschenbuch. 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.
