Data Mining with Weka

Overview

Explore the fundamentals of data mining using Weka in this comprehensive video series from the University of Waikato's Department of Computer Science. Dive into the Weka Explorer, learn to build and evaluate classifiers, and master essential techniques such as filtering, visualization, and cross-validation. Progress through various classification methods including decision trees, nearest neighbor, linear and logistic regression, support vector machines, and ensemble learning. Gain practical insights into the data mining process, understand potential pitfalls, and explore ethical considerations in the field. Suitable for beginners and intermediate learners, this 4.5-hour course provides a solid foundation in data mining concepts and hands-on experience with the Weka software.

Syllabus

Data Mining with Weka: Trailer.
Data Mining with Weka (1.1: Introduction).
Data Mining with Weka (1.2: Exploring the Explorer).
Data Mining with Weka (1.3: Exploring datasets).
Data Mining with Weka (1.4: Building a classifier).
Data Mining with Weka (1.5: Using a filter ).
Data Mining with Weka (1.6: Visualizing your data).
Data Mining with Weka (2.1: Be a classifier!).
Data Mining with Weka (2.2: Training and testing).
Data Mining with Weka (2.3: Repeated training and testing).
Data Mining with Weka (2.4: Baseline accuracy).
Data Mining with Weka (2.5: Cross-validation).
Data Mining with Weka (2.6: Cross-validation results).
Data Mining with Weka (3.1: Simplicity first!).
Data Mining with Weka (3.2: Overfitting).
Data Mining with Weka (3.3: Using probabilities).
Data Mining with Weka (3.4: Decision trees).
Data Mining with Weka (3.5: Pruning decision trees).
Data Mining with Weka (3.6: Nearest neighbor).
Data Mining with Weka (4.1: Classification boundaries).
Data Mining with Weka (4.2: Linear regression).
Data Mining with Weka (4.3: Classification by regression).
Data Mining with Weka (4.4: Logistic regression).
Data Mining with Weka (4.5: Support vector machines).
Data Mining with Weka (4.6: Ensemble learning).
Data Mining with Weka (5.1: The data mining process).
Data Mining with Weka (5.2: Pitfalls and pratfalls).
Data Mining with Weka (5.3: Data mining and ethics).
Data Mining with Weka (5.4: Summary).