Mon compte
Ma liste - 0
Catalogue
Ressources numériques
Nouveautés
Liens utiles
Mon compte
Recherche rapide
Recherche avancée
Recherche alphabétique
Historique
Information
Recherche
Auteur
Titre
Sujet
Titre de revue
Collection
Cotes BU
Cotes ENSEA
Cotes IUFM
Modifier la recherche
>
CERGY
Elargir la recherche
Sur le même sujet :
Python (langage de programmation)
Statistiques
Probabilités
Parcourir le catalogue
par auteur:
Unpingco , José H.
Rechercher sur Internet
Localiser dans une autre bibliothèque (SUDOC) (PPN ou ISBN ou ISSN)
Aperçu dans Google Books
Affichage MARC
Auteur :
Unpingco , José H.
Titre :
Python for probability, statistics and machine learning , José Unpingco
Edition :
Third edition
Editeur :
Cham : Springer Nature Switzerland
C 2022
Description :
1 vol. (XVII-509 p.) : ill. en coul. ; 25 cm
ISBN:
978-3-031-04647-6 , rel.
Notes :
Bibliogr. p. 505-506. Index
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. · Features a novel combination of modern Python implementations and underlying mathematics to illustrate and visualize the foundational ideas of probability, statistics, and machine learning; · Includes meticulously worked-out numerical examples, all reproducible using the Python code provided in the text, that compute and visualize statistical and machine learning models thus enabling the reader to not only implement these models but understand their inherent trade-offs; · Utilizes modern Python modules such as Statsmodels, Tensorflow, Keras, Sympy, and Scikit-learn, along with embedded "Programming Tips" to encourage readers to develop quality Python codes that implement and illustrate practical concepts.
Sujet :
Python (langage de programmation)
Statistiques
Probabilités
Exemplaires
Site
Emplacement
Cote
Type de prêt
Statut
Site de Saint-Martin
Espace Recherche
005.133 PYT U
Prêt
Disponible
Réserver
Pour toute question,
contactez la bibliothèque
Horizon Information Portal 3.0© 2001-2019
SirsiDynix
Tous droits réservés.