More Data Mining with Weka

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

Assess the volume of training data needed for mining tasks

Calculate optimal parameter values for a given learning system

Classify documents using various techniques

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

Debate the correspondence between decision trees and decision rules

Demonstrate how to set up learning tasks as a knowledge flow

Describe different techniques for searching through subsets of attributes

Design and evaluate multi-layer neural networks

Develop effective sets of attributes for text classification problems

Discuss techniques for representing, generating, and evaluating clusters

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

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

Explain how association rules can be generated and used

Identify the advantages of supervised vs unsupervised discretization

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

Solve data mining problems on huge datasets

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