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PySpark en pratique: Cas d'usages réels et exemples pratiques en Data Science et Machine Learning

PySpark en pratique: Cas d'usages réels et exemples pratiques en Data Science et Machine Learning

          
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About the Book

Le domaine de la Data Science et du Machine Learning a connu une croissance exponentielle ces dernières années, et il est devenu essentiel pour les professionnels de comprendre et de maîtriser les outils et les techniques nécessaires pour extraire des connaissances précieuses à partir de données complexes. C'est dans ce contexte que PySpark se distingue comme un choix puissant pour les tâches de manipulation, d'analyse et de modélisation de données à grande échelle.

Notre objectif avec ce livre est de vous fournir une ressource complète et pratique pour développer vos compétences en PySpark. Nous avons conçu ce livre en mettant l'accent sur des cas d'usages réels et des exemples pratiques, afin que vous puissiez appliquer directement les concepts et les techniques appris à des problèmes concrets.

Ce livre offre une approche pratique et concrète pour exploiter la puissance de PySpark dans le domaine de la Data Science et du Machine Learning. Il permet aux lecteurs de développer leurs compétences en utilisant des cas d'utilisation réels et en explorant des exemples pratiques. Grâce aux connaissances acquises tout au long du livre, les lecteurs seront en mesure de manipuler, analyser et modéliser des données volumineuses en utilisant les fonctionnalités avancées de PySpark.

Que vous soyez un étudiant, un professionnel en informatique ou un passionné de données, nous espérons que ce livre vous inspirera et vous accompagnera dans votre parcours d'apprentissage.

Que contient ce livre ?

  • Introduction à Apache Spark et PySpark: Le livre commence par une introduction détaillée à Apache Spark, son importance et son rôle dans le traitement des données à grande échelle. Il met également en évidence l'utilisation de PySpark, qui permet aux utilisateurs de tirer parti de la puissance de Spark en utilisant le langage de programmation Python.
  • Programmation en PySpark pour l'analyse des données: Le chapitre 2 explore les fonctionnalités de PySpark pour l'importation, la manipulation et la visualisation des données. Il présente différentes techniques pour travailler avec des dataframes Spark, en utilisant des opérations sur les colonnes, les lignes et en appliquant des fonctions intégrées ou définies par l'utilisateur.
  • Initiation au Machine Learning avec Spark MLlib: Le chapitre 3 introduit Spark MLlib, la bibliothèque de Machine Learning de Spark. Il présente les composants de MLlib, les algorithmes disponibles et guide les lecteurs à travers deux exemples pratiques. Les étapes de prétraitement des données, la construction des modèles, l'évaluation et l'inférence sont démontrées.
  • Régression avec Spark MLlib: Le chapitre 4 se concentre sur la régression avec Spark MLlib. Il explore la problématique business, la collecte et l'analyse exploratoire des données, la préparation des données pour les algorithmes de Machine Learning, l'entraînement et l'évaluation des modèles, le réglage des hyperparamètres et l'utilisation du modèle final.


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Product Details
  • ISBN-13: 9798399260143
  • Publisher: Amazon Digital Services LLC - Kdp
  • Publisher Imprint: Independently Published
  • Height: 235 mm
  • No of Pages: 212
  • Spine Width: 11 mm
  • Weight: 376 gr
  • ISBN-10: 8399260142
  • Publisher Date: 27 Jun 2023
  • Binding: Paperback
  • Language: French
  • Returnable: N
  • Sub Title: Cas d'usages réels et exemples pratiques en Data Science et Machine Learning
  • Width: 191 mm


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