語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Statistical Learning with Math and P...
~
Suzuki, Joe.
Statistical Learning with Math and Python = 100 Exercises for Building Logic /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Statistical Learning with Math and Python/ by Joe Suzuki.
其他題名:
100 Exercises for Building Logic /
作者:
Suzuki, Joe.
面頁冊數:
XI, 256 p. 446 illus., 170 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Data Structures and Information Theory. -
電子資源:
https://doi.org/10.1007/978-981-15-7877-9
ISBN:
9789811578779
Statistical Learning with Math and Python = 100 Exercises for Building Logic /
Suzuki, Joe.
Statistical Learning with Math and Python
100 Exercises for Building Logic /[electronic resource] :by Joe Suzuki. - 1st ed. 2021. - XI, 256 p. 446 illus., 170 illus. in color.online resource.
Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
ISBN: 9789811578779
Standard No.: 10.1007/978-981-15-7877-9doiSubjects--Topical Terms:
1211601
Data Structures and Information Theory.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Statistical Learning with Math and Python = 100 Exercises for Building Logic /
LDR
:02938nam a22003855i 4500
001
1051455
003
DE-He213
005
20210818011749.0
007
cr nn 008mamaa
008
220103s2021 si | s |||| 0|eng d
020
$a
9789811578779
$9
978-981-15-7877-9
024
7
$a
10.1007/978-981-15-7877-9
$2
doi
035
$a
978-981-15-7877-9
050
4
$a
Q334-342
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Suzuki, Joe.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1070643
245
1 0
$a
Statistical Learning with Math and Python
$h
[electronic resource] :
$b
100 Exercises for Building Logic /
$c
by Joe Suzuki.
250
$a
1st ed. 2021.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2021.
300
$a
XI, 256 p. 446 illus., 170 illus. in color.
$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: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
520
$a
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
650
2 4
$a
Data Structures and Information Theory.
$3
1211601
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
669775
650
2 4
$a
Machine Learning.
$3
1137723
650
1 4
$a
Artificial Intelligence.
$3
646849
650
0
$a
Data structures (Computer science).
$3
680370
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Statistics .
$3
1253516
650
0
$a
Machine learning.
$3
561253
650
0
$a
Artificial intelligence.
$3
559380
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811578762
776
0 8
$i
Printed edition:
$z
9789811578786
856
4 0
$u
https://doi.org/10.1007/978-981-15-7877-9
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入