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Titlebook: Innere Fesseln l?sen – befreit führen; Führungspotenziale e Steffen Elbert Book 2022 Sch?ffer-Poeschel Verlag für Wirtschaft – Steuern – Re

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發(fā)表于 2025-3-23 12:42:25 | 只看該作者
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發(fā)表于 2025-3-23 16:40:43 | 只看該作者
popularity of texts in data science. In this chapter, concise introductions have been given about the most popular and also successful machine learning algorithms. This chapter will be helpful for those readers who do not have enough information about machine learning and its algorithms.
13#
發(fā)表于 2025-3-23 20:19:49 | 只看該作者
Steffen Elbert as lag, date time, and windowing (rolling means). Then, we compare the performance of different time series models, such as naive (persistence), Moving Average, ARIMA, and SARIMAX. We conclude that time series analysis techniques are used correctly and can handle powerful tools for businesses to ma
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發(fā)表于 2025-3-23 23:49:17 | 只看該作者
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發(fā)表于 2025-3-24 03:01:54 | 只看該作者
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發(fā)表于 2025-3-24 07:34:46 | 只看該作者
Steffen Elbert very few rigorous instructional resources, interactive learning materials, and dynamic trainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi
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發(fā)表于 2025-3-24 13:54:04 | 只看該作者
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發(fā)表于 2025-3-24 15:48:17 | 只看該作者
data for the time period January, 1964 to December, 2017 is considered for analysis. Data mining processes such as data collection, data pre-processing, modeling, and evaluation are strictly followed for empirical studies. The forecasting performances of these models are confirmed by precision, rec
19#
發(fā)表于 2025-3-24 21:08:18 | 只看該作者
20#
發(fā)表于 2025-3-25 00:15:55 | 只看該作者
Steffen Elbertainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi978-3-030-10187-9978-3-319-72347-1
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