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Python for Probability, Statistics, and Machine Learning - José Unpingco

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    Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9783031046506_dbm

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        Présentation Python For Probability, Statistics, And Machine Learning de José Unpingco Format Broché

         - Livre Informatique

        Livre Informatique - José Unpingco - 01/11/2023 - Broché - Langue : Anglais

        . .

      • Auteur(s) : José Unpingco
      • Editeur : Springer International Publishing Ag
      • Langue : Anglais
      • Parution : 01/11/2023
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 528
      • Expédition : 791
      • Dimensions : 23.5 x 15.5 x 2.9
      • ISBN : 9783031046506



      • Résumé :

        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.
        ...

        Biographie:

        Dr. Jos? Unpingco completed his PhD from the University of California (UCSD), San Diego and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data science topics, with deep experience in machine learning. He was the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD) where he also spearheaded the DoD-wide adoption of scientific Python. In his time as the primary scientific Python instructor for the DoD, he taught over 600 scientists and engineers. Dr. Unpingco is currently the Vice President for Machine Learning/Data Science for the Gary and Mary West Health Institute, a non-profit Medical Research Organization in San Diego, California. He is also a lecturer at UCSD for their undergraduate and graduate Machine Learning and Data Science degree programs.
        ...

        Sommaire:
        Dr. Jos? Unpingco completed his PhD at the University of California, San Diego in 1997 and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data processing and analysis topics, with deep experience in machine learning and statistics. As the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD), he spearheaded the DoD-wide adoption of scientific Python. He also trained over 600 scientists and engineers to effectively utilize Python for a wide range of scientific topics -- from weather modeling to antenna analysis. Dr. Unpingco is the cofounder and Senior Director for Data Science at a non-profit Medical Research Organization in San Diego, California. He also teaches programming for data analysis at the University of California, San Diego for engineering undergraduate/graduate students. He is author of Python for Signal Processing (Springer 2014) and Python for Probability,Statistics, and Machine Learning (2016) ...

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