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The Advanced Data Mining with Weka will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualisation and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.

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

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: University of Waikato

Course Curriculum

Lec 1 More Data Mining with Weka: Trailer 00:03:00
Lec 2 More Data Mining with Weka (1.1: Introduction) 00:06:00
Lec 3 More Data Mining with Weka (1.2: Exploring the Experimenter) 00:11:00
Lec 4 More Data Mining with Weka (1.3: Comparing classifiers) 00:08:00
Lec 5 More Data Mining with Weka (1.4: The Knowledge Flow interface) 00:08:00
Lec 6 More Data Mining with Weka (1.5: The Command Line interface) 00:11:00
Lec 7 More Data Mining with Weka (1.6: Working with big data) 00:11:00
Lec 8 More Data Mining with Weka (2.1: Discretizing numeric attributes) 00:10:00
Lec 9 More Data Mining with Weka (2.2: Supervised discretization and the FilteredClassifier) 00:08:00
Lec 10 More Data Mining with Weka (2.3: Discretization in J48) 00:08:00
Lec 11 More Data Mining with Weka (2.4: Document classification) 00:13:00
Lec 12 More Data Mining with Weka (2.5: Evaluating 2‐class classification) 00:12:00
Lec 13 More Data Mining with Weka (2.6: Multinomial Naïve Bayes) 00:10:00
Lec 14 More Data Mining with Weka (3.1: Decision trees and rules) 00:08:00
Lec 15 More Data Mining with Weka (3.2: Generating decision rules) 00:08:00
Lec 16 More Data Mining with Weka (3.3: Association rules) 00:06:00
Lec 17 More Data Mining with Weka (3.4: Learning association rules) 00:10:00
Lec 18 More Data Mining with Weka (3.5: Representing clusters) 00:08:00
Lec 19 More Data Mining with Weka (3.6: Evaluating clusters) 00:10:00
Lec 20 More Data Mining with Weka (4.1: Attribute selection using the “wrapper” method) 00:11:00
Lec 21 More Data Mining with Weka (4.2: The Attribute Selected Classifier) 00:09:00
Lec 22 More Data Mining with Weka (4.3: Scheme-independent attribute selection) 00:08:00
Lec 23 More Data Mining with Weka (4.4: Fast attribute selection using ranking) 00:07:00
Lec 24 More Data Mining with Weka (4.5: Counting the cost) 00:07:00
Lec 25 More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning) 00:11:00
Lec 26 More Data Mining with Weka (5.1: Simple neural networks) 00:09:00
Lec 27 More Data Mining with Weka (5.2: Multilayer Perceptrons) 00:10:00
Lec 28 More Data Mining with Weka (5.3: Learning curves) 00:07:00
Lec 29 More Data Mining with Weka (5.4: Meta-learners for performance optimization) 00:10:00
Lec 30 More Data Mining with Weka (5.5: ARFF and XRFF) 00:06:00
Lec 31 More Data Mining with Weka (5.6: Summary) 00:07:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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