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Python for Probability, Statistics, and Machine Learning
Af: Jose Unpingco Engelsk Hardback
SPAR
kr 219
Python for Probability, Statistics, and Machine Learning
Af: Jose Unpingco Engelsk Hardback

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.

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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.

Produktdetaljer
Sprog: Engelsk
Sider: 509
ISBN-13: 9783031046476
Indbinding: Hardback
Udgave:
ISBN-10: 3031046471
Udg. Dato: 5 nov 2022
Længde: 33mm
Bredde: 162mm
Højde: 241mm
Oplagsdato: 5 nov 2022
Forfatter(e): Jose Unpingco
Forfatter(e) Jose Unpingco


Kategori Matematik til informatikfag


Sprog Engelsk


Indbinding Hardback


Sider 509


Udgave


Længde 33mm


Bredde 162mm


Højde 241mm

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