This course on More Data Mining with Weka aims to enhance learners' understanding of data mining techniques using the Weka software. By the end of the course, students will be able to compare classifiers, work with big data, discretize numeric attributes, generate decision rules, learn association rules, evaluate clusters, perform attribute selection, utilize neural networks, and optimize performance using meta-learners. The course employs a hands-on approach through practical exercises and demonstrations. It is designed for individuals interested in advancing their data mining skills, particularly those with a basic understanding of Weka and data mining concepts.
Overview
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
