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Titlebook: Machine Learning with Microsoft Technologies; Selecting the Right Leila Etaati Book 2019 Leila Etaati 2019 Microsoft Advance Analytics Arc

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發(fā)表于 2025-3-21 19:50:42 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning with Microsoft Technologies
副標(biāo)題Selecting the Right
編輯Leila Etaati
視頻videohttp://file.papertrans.cn/621/620714/620714.mp4
概述Offers methods for choosing the right architecture for a machine learning solution using Microsoft technologies.Gives you valuable knowledge for creating, developing, and deploying machine learning in
圖書封面Titlebook: Machine Learning with Microsoft Technologies; Selecting the Right  Leila Etaati Book 2019 Leila Etaati 2019 Microsoft Advance Analytics Arc
描述.Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more..The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set...Machine Learning with Microsoft Technologies. is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. ..Detailed content is provided on the main algorithms fo
出版日期Book 2019
關(guān)鍵詞Microsoft Advance Analytics Architecture; R services; Machine Learning Services; Azure Data Lake; Spark;
版次1
doihttps://doi.org/10.1007/978-1-4842-3658-1
isbn_softcover978-1-4842-3657-4
isbn_ebook978-1-4842-3658-1
copyrightLeila Etaati 2019
The information of publication is updating

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Data Wrangling for Predictive AnalysisIn the machine learning process, after business understanding, the next step is collecting the right data, feature selection, and data wrangling. Data wrangling includes data cleaning, joining different data sources, quality control, data integration, data transformation, and data reduction processes (Figure 6-1).
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Introduction to Machine Learningrch and specific industries. In most fields, there is a valuable opportunity to use machine learning to obtain more concise and in-depth information from available data. As a result, most big software companies provide opportunities to their users to access machine learning via easy-to-use software.
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