Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berkeley, CA
Apress
2021
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484265000/?ar |
| Zusammenfassung: | 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 Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. You will: Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations. |
| Beschreibung: | Gradient Descent. - Includes index. - Print version record |
| Umfang: | 1 Online-Ressource (427 pages) |
| ISBN: | 9781484265000 1484265009 1484265017 |
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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, Sridhar Alla Berkeley, CA Apress 2021 1 Online-Ressource (427 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gradient Descent. - Includes index. - Print version record 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 Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. You will: Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations. Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Données volumineuses Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) Computer software Krishnan, Sundar MitwirkendeR ctb Alla, Sridhar MitwirkendeR ctb 9781484264997 Erscheint auch als Druck-Ausgabe 9781484264997 |
| spellingShingle | Kakarla, Ramcharan 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) Données volumineuses Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) Computer software |
| 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, Sridhar Alla |
| title_fullStr | Applied data science using Pyspark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla |
| title_full_unstemmed | Applied data science using Pyspark learn the end-to-end predictive model-building cycle Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla |
| 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) Données volumineuses Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) Computer software |
| topic_facet | Big data Machine learning Python (Computer program language) Parallel processing (Electronic computers) Données volumineuses Apprentissage automatique Python (Langage de programmation) Parallélisme (Informatique) Computer software |
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