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This Fundamental Concept of Data Mining with Weka is designed to introduce you with practical data mining using the Weka workbench. The course will dispel the mystery that surrounds data mining and explain the basic principles of several popular algorithms and how to use them in practical applications. You’ll learn about filters for preprocessing data, selecting attributes, classification, clustering, association rules, cost-sensitive evaluation. You’ll meet learning curves and automatically optimize learning parameters. After completing the course will be able to mine your own data.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: University of Waikato

Course Curriculum

Lec 1 Data Mining with Weka: Trailer 00:05:00
Lec 2 Data Mining with Weka (1.1: Introduction) 00:09:00
Lec 3 Data Mining with Weka (1.2: Exploring the Explorer) 00:11:00
Lec 4 Data Mining with Weka (1.3: Exploring datasets) 00:11:00
Lec 5 Data Mining with Weka (1.4: Building a classifier) 00:09:00
Lec 6 Data Mining with Weka (1.5: Using a filter ) 00:07:00
Lec 7 Data Mining with Weka (1.6: Visualizing your data) 00:08:00
Lec 8 Data Mining with Weka (2.1: Be a classifier!) 00:11:00
Lec 9 Data Mining with Weka (2.2: Training and testing) 00:06:00
Lec 10 Data Mining with Weka (2.3: Repeated training and testing) 00:07:00
Lec 11 Data Mining with Weka (2.4: Baseline accuracy) 00:08:00
Lec 12 Data Mining with Weka (2.5: Cross-validation) 00:07:00
Lec 13 Data Mining with Weka (2.6: Cross-validation results) 00:07:00
Lec 14 Data Mining with Weka (3.1: Simplicity first!) 00:08:00
Lec 15 Data Mining with Weka (3.2: Overfitting) 00:08:00
Lec 16 Data Mining with Weka (3.3: Using probabilities) 00:12:00
Lec 17 Data Mining with Weka (3.4: Decision trees) 00:09:00
Lec 18 Data Mining with Weka (3.5: Pruning decision trees) 00:11:00
Lec 19 Data Mining with Weka (3.6: Nearest neighbor) 00:09:00
Lec 20 Data Mining with Weka (4.1: Classification boundaries) 00:12:00
Lec 21 Data Mining with Weka (4.2: Linear regression) 00:09:00
Lec 22 Data Mining with Weka (4.3: Classification by regression) 00:11:00
Lec 23 Data Mining with Weka (4.4: Logistic regression) 00:10:00
Lec 24 Data Mining with Weka (4.5: Support vector machines) 00:08:00
Lec 25 Data Mining with Weka (4.6: Ensemble learning) 00:10:00
Lec 26 Data Mining with Weka (5.1: The data mining process) 00:08:00
Lec 27 Data Mining with Weka (5.2: Pitfalls and pratfalls) 00:10:00
Lec 28 Data Mining with Weka (5.3: Data mining and ethics) 00:08:00
Lec 29 Data Mining with Weka (5.4: Summary) 00:07:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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