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Data Mining and the Analytics Workflow


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Default Data Mining and the Analytics Workflow - 11-08-2019, 10:00 PM

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Data Mining and the Analytics Workflow
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 55m | 400 MB
Instructor: Janani Ravi

This course explains the continuing relevance of Data Mining today, in the context of applying machine learning techniques to big data. It covers conceptual and practical details of powerful techniques such as Association Rules learning and the industry-standard CRISP-DM methodology for data mining workflows.
Data Mining is an umbrella term used for techniques that find patterns in large datasets. Simply put, data mining is the application of machine learning techniques on big data. The popularity of the term Data Mining peaked some years ago, but in substance, data mining is perhaps more relevant today than it has ever been. In this course, Data Mining and the Analytics Workflow you will gain the ability to formulate your use-case as a Data Mining problem, and then apply a classic process, the CRISP-DM methodology, to solve it. First, you will learn how association rules learning works, and why it is considered a classic data mining application, predating the explosion in the popularity of ML. You will see the similarities and contrasts between association rules learning and recommender systems. Next, you will discover how big data and machine learning both squarely lie within the ambit of data mining, even as more traditional data mining links to statistics and information retrieval continue to exist. Finally, you will round out your knowledge by learning about an industry-standard process for building data mining applications, know as the CRISP-DM. This technique is about two decades old but has retained its relevance, and closely mirrors the classic machine learning workflow in wide use today. When you're finished with this course, you will have the skills and knowledge to design and implement the right data mining solution, one that applies machine learning on big data, for your use-case.
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