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WEKA - Data Mining With Open Source Machine Learning Tool

WEKA - Data Mining With Open Source Machine Learning Tool

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

Overview

Description
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.

Weka is open source software issued under the GNU General Public License.

We have put together several free online courses that teach machine learning and data mining using R Programming, Python Programming, Weka Toolkit and SQL.

Yes, it is possible to apply Weka to process big data and perform deep learning!

Course Information

Basic Mathematics is enough

Students can learn WEKA tool for data pre-processing, classification, regression, clustering, association rules, and visualization

Graduates or Pursuing BTech Students

• Lifetime Access to Each Course
• Certificate on Completion of Course
• No Extra Charges Or Admin Fees
• Easy Access to Courses
• High Priority Support After Sales.
• Big Discounts on Individual Courses

Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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