This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms. Students will work with multimillion-instance datasets, classify text, experiment with clustering, association rules, neural networks, and much more.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Prose Simiancompleted this course, spending 4 hours a week on it and found the course difficulty to be medium.
Another well thought-out Weka course from Waikato covering further areas of data mining (association rules, clustering, text classification, cost-sensitive techniques), of Weka (experimenter & knowledge flow interfaces) and of the data mining evaluation process (ROC, learning curves).
Assessments are fiddly, with a strong applied emphasis, but not too hard.
Current session closes 15/4/16, with an 'Advanced' follow-up course scheduled for late April (per Dr Witten's forum comments)