Python Probability Statistics Machine by Unpingco José (55 results)
- Hardcover
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, GermanyUniversitätsbuchhandlung Herta Hold GmbH
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2nd 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.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
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Language: English
Published by Springer International Publishing AG, Cham, 2023
- Softcover
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Paperback. Condition: 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 t…heir 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.
- Softcover
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
- Softcover
Seller: Hawking Books, Edgewood, TX, U.S.A.Hawking Books
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- Softcover
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- Softcover
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- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: New. pp. 528.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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- Softcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Softcover
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Language: English
Published by Springer International Publishing AG, Cham, 2022
- Hardcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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US$ 113.40
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Hardcover. Condition: 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 t…heir 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.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 108.58
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Paperback. Condition: Brand New. 2nd edition. 400 pages. 9.25x6.10x0.94 inches. In Stock.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 3rd edition. 526 pages. 9.26x6.10x1.06 inches. In Stock.
Language: English
Published by Springer International Publishing AG, CH, 2022
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: New. Third Edition 2022.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 3rd edition. 526 pages. 9.25x6.10x1.30 inches. In Stock.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 75.46
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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.
