More Data Mining with Weka

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Course Features

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Duration

5 weeks

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Delivery Method

Online

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Available on

Lifetime Access

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Accessibility

Mobile, Desktop

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Language

English

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Subtitles

English

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Level

Beginner

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Effort

4 hours per week

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Teaching Type

Self Paced

Course Description

This course is led by the University of Waikato, where Weka was born. It will teach you advanced data mining skills and techniques. You'll be able to process a 10 million-instance dataset and mine a 250,000 word text dataset. This course follows the Data Mining with Weka first course. Analyzing a supermarket dataset with 5000 shopping carts will help you to understand how to filter data, select attributes, clustering, association rules and cost-sensitive evaluation. Learn how to optimize learning parameters and learn curves. You can preview some of the course steps here before you sign up.

Course Overview

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International Faculty

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

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Compare the performance of different mining methods on a wide range of datasets

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Demonstrate how to set up learning tasks as a knowledge flow

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Solve data mining problems on huge datasets

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Apply equal-width and equal-frequency binning for discretizing numeric attributes

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Identify the advantages of supervised vs unsupervised discretization

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Evaluate different trade-offs between error rates in 2-class classification

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Classify documents using various techniques

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Debate the correspondence between decision trees and decision rules

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Explain how association rules can be generated and used

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Discuss techniques for representing, generating, and evaluating clusters

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Perform attribute selection by wrapping a classifier inside a cross-validation loop

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Describe different techniques for searching through subsets of attributes

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Develop effective sets of attributes for text classification problems

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Explain cost-sensitive evaluation, cost-sensitive classification, and cost-sensitive learning

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Design and evaluate multi-layer neural networks

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Assess the volume of training data needed for mining tasks

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Calculate optimal parameter values for a given learning system

Target Students

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This course is aimed at anyone who deals in data professionally or is interested in furthering their professional or academic skills in data science

Course Instructors

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Ian Witten

Instructor

I grew up in Ireland, studied at Cambridge, and taught computer science at the Universities of Essex in England and Calgary in Canada before moving to paradise (aka New Zealand) 25 years ago.

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