Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Applied Data Science Using PySpark =...
~
Alla, Sridhar.
Applied Data Science Using PySpark = Learn the End-to-End Predictive Model-Building Cycle /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Applied Data Science Using PySpark/ by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla.
Reminder of title:
Learn the End-to-End Predictive Model-Building Cycle /
Author:
Kakarla, Ramcharan.
other author:
Krishnan, Sundar.
Description:
XXVI, 410 p. 190 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Computer software. -
Online resource:
https://doi.org/10.1007/978-1-4842-6500-0
ISBN:
9781484265000
Applied Data Science Using PySpark = Learn the End-to-End Predictive Model-Building Cycle /
Kakarla, Ramcharan.
Applied Data Science Using PySpark
Learn the End-to-End Predictive Model-Building Cycle /[electronic resource] :by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla. - 1st ed. 2021. - XXVI, 410 p. 190 illus.online resource.
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.
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 .
ISBN: 9781484265000
Standard No.: 10.1007/978-1-4842-6500-0doiSubjects--Topical Terms:
528062
Computer software.
LC Class. No.: QA76.75-.765
Dewey Class. No.: 004
Applied Data Science Using PySpark = Learn the End-to-End Predictive Model-Building Cycle /
LDR
:03572nam a22003855i 4500
001
1050798
003
DE-He213
005
20210624001622.0
007
cr nn 008mamaa
008
220103s2021 xxu| s |||| 0|eng d
020
$a
9781484265000
$9
978-1-4842-6500-0
024
7
$a
10.1007/978-1-4842-6500-0
$2
doi
035
$a
978-1-4842-6500-0
050
4
$a
QA76.75-.765
072
7
$a
U
$2
bicssc
072
7
$a
COM000000
$2
bisacsh
072
7
$a
UX
$2
thema
082
0 4
$a
004
$2
23
100
1
$a
Kakarla, Ramcharan.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1355199
245
1 0
$a
Applied Data Science Using PySpark
$h
[electronic resource] :
$b
Learn the End-to-End Predictive Model-Building Cycle /
$c
by Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla.
250
$a
1st ed. 2021.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2021.
300
$a
XXVI, 410 p. 190 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
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.
520
$a
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 .
650
0
$a
Computer software.
$3
528062
650
0
$a
Big data.
$3
981821
650
0
$a
Machine learning.
$3
561253
650
1 4
$a
Professional Computing.
$3
1115983
650
2 4
$a
Big Data.
$3
1017136
650
2 4
$a
Machine Learning.
$3
1137723
700
1
$a
Krishnan, Sundar.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1355200
700
1
$a
Alla, Sridhar.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1308115
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484264997
776
0 8
$i
Printed edition:
$z
9781484265017
856
4 0
$u
https://doi.org/10.1007/978-1-4842-6500-0
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login