Dive deeper into advanced data mining techniques using Weka in this comprehensive 4.5-hour video series from the University of Waikato. Explore the Experimenter interface, compare classifiers, and master the Knowledge Flow and Command Line interfaces. Learn to work with big data, discretize numeric attributes, and apply supervised discretization. Delve into document classification, evaluate 2-class and multinomial classification, and understand decision trees and rules. Discover association rules, cluster representation and evaluation, and various attribute selection methods. Investigate cost-sensitive classification and learning, explore neural networks including Multilayer Perceptrons, and optimize performance with meta-learners. Gain proficiency in ARFF and XRFF file formats while expanding your data mining toolkit.
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
