More Data Mining with Weka

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

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