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...
Otros Autores: | , , |
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Formato: | Libro electrónico |
Idioma: | Inglés |
Publicado: |
[Place of publication not identified] :
Apress
[2021]
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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.