東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multivariate analysis and machine le...

Sundararajan, Srikrishnan.

FindBook

Google Book

Amazon

博客來

Multivariate analysis and machine learning techniques = feature analysis in data science using Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate analysis and machine learning techniques/ by Srikrishnan Sundararajan.
其他題名:	feature analysis in data science using Python /
作者:	Sundararajan, Srikrishnan.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xxvi, 435 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction -- Chapter 2: Python for Data Analytics - A Quick Tour -- Chapter 3: Probability -- Chapter 4: Statistical Concepts -- Chapter 5: Correlation and Regression -- Chapter 6: Classification -- Chapter 7: Factor Analysis -- Chapter 8: Cluster Analysis -- Chapter 9: Survival Analysis -- Chapter 10: Computational Techniques -- Chapter 11: Machine Learning.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-99-0353-5
ISBN:	9789819903535

Multivariate analysis and machine learning techniques = feature analysis in data science using Python /
Sundararajan, Srikrishnan.

Multivariate analysis and machine learning techniquesfeature analysis in data science using Python /[electronic resource] :by Srikrishnan Sundararajan. - Singapore :Springer Nature Singapore :2025. - xxvi, 435 p. :ill., digital ;24 cm. - Transactions on computer systems and networks,2730-7492. - Transactions on computer systems and networks..

Chapter 1: Introduction -- Chapter 2: Python for Data Analytics - A Quick Tour -- Chapter 3: Probability -- Chapter 4: Statistical Concepts -- Chapter 5: Correlation and Regression -- Chapter 6: Classification -- Chapter 7: Factor Analysis -- Chapter 8: Cluster Analysis -- Chapter 9: Survival Analysis -- Chapter 10: Computational Techniques -- Chapter 11: Machine Learning.

This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.

ISBN: 9789819903535

Standard No.: 10.1007/978-981-99-0353-5doiSubjects--Topical Terms:

677684
Multivariate analysis
--Data processing.

LC Class. No.: QA278

Dewey Class. No.: 519.535028

Multivariate analysis and machine learning techniques = feature analysis in data science using Python /
LDR:03198nmm a2200337 a 4500 001 2409768
003 DE-He213
005 20250529091257.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819903535 $q (electronic bk.)
020 $a 9789819903528 $q (paper)
024 7 $a 10.1007/978-981-99-0353-5 $2 doi
035 $a 978-981-99-0353-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 519.535028 $2 23
090 $a QA278 $b .S957 2025
100 1 $a Sundararajan, Srikrishnan. $3 3783178
245 1 0 $a Multivariate analysis and machine learning techniques $h [electronic resource] : $b feature analysis in data science using Python / $c by Srikrishnan Sundararajan.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xxvi, 435 p. : $b ill., digital ; $c 24 cm.
490 1 $a Transactions on computer systems and networks, $x 2730-7492
505 0 $a Chapter 1: Introduction -- Chapter 2: Python for Data Analytics - A Quick Tour -- Chapter 3: Probability -- Chapter 4: Statistical Concepts -- Chapter 5: Correlation and Regression -- Chapter 6: Classification -- Chapter 7: Factor Analysis -- Chapter 8: Cluster Analysis -- Chapter 9: Survival Analysis -- Chapter 10: Computational Techniques -- Chapter 11: Machine Learning.
520 $a This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.
650 0 $a Multivariate analysis $x Data processing. $3 677684
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Python. $3 3201289
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Computational Intelligence. $3 1001631
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Transactions on computer systems and networks. $3 3514031
856 4 0 $u https://doi.org/10.1007/978-981-99-0353-5
950 $a Computer Science (SpringerNature-11645)