國立虎尾科技大學 |

Data Science Revealed = With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data Science Revealed/ by Tshepo Chris Nokeri.
其他題名:	With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning /
作者:	Nokeri, Tshepo Chris.
面頁冊數:	XX, 252 p. 95 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Python. -
電子資源:	https://doi.org/10.1007/978-1-4842-6870-4
ISBN:	9781484268704

Data Science Revealed = With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning /
Nokeri, Tshepo Chris.

Data Science RevealedWith Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning /[electronic resource] :by Tshepo Chris Nokeri. - 1st ed. 2021. - XX, 252 p. 95 illus.online resource.

Chapter 1: An Introduction to Simple Linear Regression Analysis -- Chapter 2: Advanced Parametric Methods -- Chapter 3: Time Series Analysis -- Chapter 4: High-Quality Time Series Analysis -- Chapter 5: Logistic Regression Analysis -- Chapter 6: Dimension Reduction and Multivariate Analysis Using Linear Discriminant Analysis -- Chapter 7: Finding Hyperplanes Using Support Vectors -- Chapter 8: Classification Using Decision Trees -- Chapter 9: Back to the Classics -- Chapter 10: Cluster Analysis -- Chapter 11: Survival Analysis -- Chapter 12: Neural Networks -- Chapter 13: Machine Learning Using H2O.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. You will: Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization.

ISBN: 9781484268704

Standard No.: 10.1007/978-1-4842-6870-4doiSubjects--Topical Terms:

1115944
Python.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Data Science Revealed = With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning /
LDR:03988nam a22004095i 4500 001 1047089
003 DE-He213
005 20210826011128.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484268704 $9 978-1-4842-6870-4
024 7 $a 10.1007/978-1-4842-6870-4 $2 doi
035 $a 978-1-4842-6870-4
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Nokeri, Tshepo Chris. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1350730
245 1 0 $a Data Science Revealed $h [electronic resource] : $b With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning / $c by Tshepo Chris Nokeri.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XX, 252 p. 95 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: An Introduction to Simple Linear Regression Analysis -- Chapter 2: Advanced Parametric Methods -- Chapter 3: Time Series Analysis -- Chapter 4: High-Quality Time Series Analysis -- Chapter 5: Logistic Regression Analysis -- Chapter 6: Dimension Reduction and Multivariate Analysis Using Linear Discriminant Analysis -- Chapter 7: Finding Hyperplanes Using Support Vectors -- Chapter 8: Classification Using Decision Trees -- Chapter 9: Back to the Classics -- Chapter 10: Cluster Analysis -- Chapter 11: Survival Analysis -- Chapter 12: Neural Networks -- Chapter 13: Machine Learning Using H2O.
520 $a Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. You will: Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization.
650 2 4 $a Python. $3 1115944
650 1 4 $a Machine Learning. $3 1137723
650 0 $a Python (Computer program language). $3 1127623
650 0 $a Machine learning. $3 561253
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484268698
776 0 8 $i Printed edition: $z 9781484268711
776 0 8 $i Printed edition: $z 9781484277362
856 4 0 $u https://doi.org/10.1007/978-1-4842-6870-4
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)