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More Data Mining with Weka

University of Waikato via YouTube

Syllabus

More Data Mining with Weka: Trailer.
More Data Mining with Weka (1.1: Introduction).
More Data Mining with Weka (1.2: Exploring the Experimenter).
More Data Mining with Weka (1.3: Comparing classifiers).
More Data Mining with Weka (1.4: The Knowledge Flow interface).
More Data Mining with Weka (1.5: The Command Line interface).
More Data Mining with Weka (1.6: Working with big data).
More Data Mining with Weka (2.1: Discretizing numeric attributes).
More Data Mining with Weka (2.2: Supervised discretization and the FilteredClassifier).
More Data Mining with Weka (2.3: Discretization in J48).
More Data Mining with Weka (2.4: Document classification).
More Data Mining with Weka (2.5: Evaluating 2‐class classification).
More Data Mining with Weka (2.6: Multinomial Naïve Bayes).
More Data Mining with Weka (3.1: Decision trees and rules).
More Data Mining with Weka (3.2: Generating decision rules).
More Data Mining with Weka (3.3: Association rules).
More Data Mining with Weka (3.4: Learning association rules).
More Data Mining with Weka (3.5: Representing clusters).
More Data Mining with Weka (3.6: Evaluating clusters).
More Data Mining with Weka (4.1: Attribute selection using the "wrapper" method).
More Data Mining with Weka (4.2: The Attribute Selected Classifier).
More Data Mining with Weka (4.3: Scheme-independent attribute selection).
More Data Mining with Weka (4.4: Fast attribute selection using ranking).
More Data Mining with Weka (4.5: Counting the cost).
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning).
More Data Mining with Weka (5.1: Simple neural networks).
More Data Mining with Weka (5.2: Multilayer Perceptrons).
More Data Mining with Weka (5.3: Learning curves).
More Data Mining with Weka (5.4: Meta-learners for performance optimization).
More Data Mining with Weka (5.5: ARFF and XRFF).
More Data Mining with Weka (5.6: Summary).

Taught by

WEKA MOOC

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