000			01925 a2200217 4500
003			OSt
005			20250728121947.0
008			250724b |||||||| |||| 00| 0 eng d
020			_a9783031046506
082			_a005.133 _bUn6p3
100			_aUnpingco, José
245			_aPython for probability, statistics, and machine learning [3rd ed.] _cJosé Unpingco
250			_a3rd ed.
260			_bSpringer _c2022 _aSwitzerland
300			_axvii, 509p
520			_aUsing 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.
650			_aPython
650			_aProbabilities-data processing
650			_aStatistics-data processing
942			_cREF
999			_c567559 _d567559