Applied data science using Pyspark learn the end-to-end predictive model-building cycle

Discover the capabilities of PySpark and its application in the realm of data science. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. Applied Data Science U...

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Detalles Bibliográficos
Otros Autores: Kakarla, Ramcharan, author (author), Krishnan, Sundar, author, Alla, Sridhar, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: [Place of publication not identified] : Apress [2021]
Edición:1st ed. 2021.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631166906719
Tabla de Contenidos:
  • Chapter 1: Setting up the Pyspark Environment
  • Chapter 2: Basic Statistics and Visualizations
  • Chapter 3: :Variable Selection
  • Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques
  • Chapter 5: Model Validation and selecting the best model
  • Chapter 6: Unsupervised and recommendation algorithms
  • Chapter 7:End to end modeling pipelines
  • Chapter 8: Productionalizing a machine learning model
  • Chapter 9: Experimentations
  • Chapter 10:Other Tips: Optional.