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Data science revealed = with feature...

Nokeri, Tshepo Chris.

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Data science revealed = with feature engineering, data visualization, pipeline development, and hyperparameter tuning /

紀錄類型:	書目-電子資源 : 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.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xx, 252 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining. -
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. - Berkeley, CA :Apress :2021. - xx, 252 p. :ill., digital ;24 cm.

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:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Data science revealed = with feature engineering, data visualization, pipeline development, and hyperparameter tuning /
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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.
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