Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Berkeley, CA]
Apress
2024
|
| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9798868808203/?ar |
| Zusammenfassung: | 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. In Chapters 1, 2 & 3, we will get started with setting up the environment, and the basics of PySpark focusing on data manipulations. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapters 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. Chapters 8 and 9 will walk you through machine learning pipelines, and various methods available to operationalize the model and serve it through docker/API. Chapter 10 will demonstrate how you can unlock the power of predictive models when used in coherence to create a meaningful impact on your business. Chapter 11 will introduce you to some of the most used and powerful modelling frameworks to unlock real value from data. In this new edition, you will learn predictive modelling frameworks that can quantify customer lifetime values and estimate the return of your predictive modelling investments. This edition also contains methods to measure engagement and identify actionable populations for churn treatments effectively. In addition, a dedicated chapter for experimentation design including steps to efficiently design, conduct, test and measure the results of your models is added. All the codes will be refreshed as needed to reflect the latest stable version of Spark. You will: Learn the overview of end to end predictive model building Understand Multiple variable selection techniques & implementations Work with Operationalizing models Perform Data science experimentations & tips. |
| Beschreibung: | Includes index. - Online resource; title from PDF title page (SpringerLink, viewed December 12, 2024) |
| Umfang: | 1 Online-Ressource (xviii, 449 pages) illustrations |
| ISBN: | 9798868808203 |
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| spelling | Kakarla, Ramcharan VerfasserIn aut Applied data science using PySpark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu Second edition. [Berkeley, CA] Apress 2024 1 Online-Ressource (xviii, 449 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Online resource; title from PDF title page (SpringerLink, viewed December 12, 2024) 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. In Chapters 1, 2 & 3, we will get started with setting up the environment, and the basics of PySpark focusing on data manipulations. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapters 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. Chapters 8 and 9 will walk you through machine learning pipelines, and various methods available to operationalize the model and serve it through docker/API. Chapter 10 will demonstrate how you can unlock the power of predictive models when used in coherence to create a meaningful impact on your business. Chapter 11 will introduce you to some of the most used and powerful modelling frameworks to unlock real value from data. In this new edition, you will learn predictive modelling frameworks that can quantify customer lifetime values and estimate the return of your predictive modelling investments. This edition also contains methods to measure engagement and identify actionable populations for churn treatments effectively. In addition, a dedicated chapter for experimentation design including steps to efficiently design, conduct, test and measure the results of your models is added. All the codes will be refreshed as needed to reflect the latest stable version of Spark. You will: Learn the overview of end to end predictive model building Understand Multiple variable selection techniques & implementations Work with Operationalizing models Perform Data science experimentations & tips. Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) Krishnan, Sundar VerfasserIn aut Dhamodharan, Balaji VerfasserIn aut Gunnu, Venkata VerfasserIn aut 9798868808197 Erscheint auch als Druck-Ausgabe 9798868808197 |
| spellingShingle | Kakarla, Ramcharan Krishnan, Sundar Dhamodharan, Balaji Gunnu, Venkata Applied data science using PySpark learn the end-to-end predictive model-building cycle Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) |
| title | Applied data science using PySpark learn the end-to-end predictive model-building cycle |
| title_auth | Applied data science using PySpark learn the end-to-end predictive model-building cycle |
| title_exact_search | Applied data science using PySpark learn the end-to-end predictive model-building cycle |
| title_full | Applied data science using PySpark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu |
| title_fullStr | Applied data science using PySpark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu |
| title_full_unstemmed | Applied data science using PySpark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu |
| title_short | Applied data science using PySpark |
| title_sort | applied data science using pyspark learn the end to end predictive model building cycle |
| title_sub | learn the end-to-end predictive model-building cycle |
| topic | Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) |
| topic_facet | Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) |
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