Data Mining

Categories: Data Mining
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Course Description

Description

Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting and generally improve their performance through interaction with data. It is currently regarded as the key elements of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process with examples. Special emphasis will be given to the Machine Learning methods as they provide real knowledge discovery tools. Important related technologies, such as data warehousing and online analytical processing (OLAP), will be also discussed. The students will use recent Data Mining software.

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frequently Asked Questions

Have Any Question? Find Answer Here

We don’t just work with concrete and steel. We work with people We are Approachable, with even our highest work

We don’t just work with concrete and steel. We work with people We are Approachable, with even.

We don’t just work with concrete and steel. We work with people We are Approachable, with even.

We don’t just work with concrete and steel. We work with people We are Approachable, with even.

We don’t just work with concrete and steel. We work with people We are Approachable, with even.

HashLearning, a project of Hashtronics, provides tailored software solutions to help organizations succeed. Let us bring your vision to life with innovative technology.

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HashLearning, a project of Hashtronics, provides tailored software solutions to help organizations succeed. Let us bring your vision to life with innovative technology.

Get updates On New Courses and News

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© Hashlearning  2021. All Rights Reserved, All Wrongs Reserved.

© Hashlearning  2021-24. All Rights Reserved, All Wrongs Reserved.