| 書目名稱 | Data Science Revealed | | 副標(biāo)題 | With Feature Enginee | | 編輯 | Tshepo Chris Nokeri | | 視頻video | http://file.papertrans.cn/264/263067/263067.mp4 | | 概述 | Covers the parametric, ensemble, and the non-parametric methods.Presents techniques to improve model performance in pre- and post-training.Summarizes H2O driverless AI and automatic forecasting using | | 圖書封面 |  | | 描述 | .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 th | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | Machine Learning; Python; Data Science; Deep Neural Networks; Regression; Classification; Time Series Anal | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-6870-4 | | isbn_softcover | 978-1-4842-6869-8 | | isbn_ebook | 978-1-4842-6870-4 | | copyright | Tshepo Chris Nokeri 2021 |
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