This learner has completed ID verification. Find out more.

Certificate of Achievement

Diego Diaz

has completed the following course:

More Data Mining with Weka

The University of Waikato

This course introduced advanced data mining skills – methods for turning raw data into useful information. Participants learned how to use advanced facilities of the Weka data mining workbench to solve a variety of large-scale data mining problems in various domains.

5 weeks, 4 hours per week

Educators

Ian H. Witten

Professor of Computer Science

The University of Waikato

0%

91%

average test score

Transcript

Learning outcomes

  • Compare the performance of different mining methods on a wide range of datasets
  • Demonstrate how to set up learning tasks as a knowledge flow
  • Solve data mining problems on huge datasets
  • Apply equal-width and equal-frequency binning for discretizing numeric attributes
  • Identify the advantages of supervised vs unsupervised discretization
  • Evaluate different trade-offs between error rates in 2-class classification
  • Classify documents using various techniques
  • Debate the correspondence between decision trees and decision rules
  • Explain how association rules can be generated and used
  • Discuss techniques for representing, generating, and evaluating clusters
  • Perform attribute selection by wrapping a classifier inside a cross-validation loop
  • Describe different techniques for searching through subsets of attributes
  • Develop effective sets of attributes for text classification problems
  • Explain cost-sensitive evaluation, cost-sensitive classification, and cost-sensitive learning
  • Design and evaluate multi-layer neural networks
  • Assess the volume of training data needed for mining tasks
  • Calculate optimal parameter values for a given learning system

Syllabus

  • Running large-scale data mining experiments
  • Constructing and executing knowledge flows
  • Processing very large datasets
  • Analyzing collections of textual documents
  • Mining association rules
  • Preprocessing data using a range of filters
  • Automatic methods of attribute selection
  • Clustering data
  • Taking account of different decision costs
  • Producing learning curves
  • Optimizing learning parameters in data mining

Issued on 26th March 2018

The person named on this certificate has completed the activities in the transcript above. For more information about Certificates of Achievement and the effort required to become eligible, visit futurelearn.com/proof-of-learning/certificate-of-achievement.

This learner has verified their identity. For more information about how FutureLearn verifies identities, visit futurelearn.com/verification/how-it-works. The certificate and transcript do not imply the award of credit or the conferment of a qualification from The University of Waikato.

Free online course:

More Data Mining with Weka

The University of Waikato