Thursday, 21 May 2015

Enrolments open for new session of More Data with Weka

We will be closing the current session of Data Mining with Weka on the 25th May.
A new session of More Data Mining with Weka is now open for enrolment and will start on 1 June 2015.

You do not have to have actually obtained a Statement of Completion for the introductory Data Mining with Weka MOOC to embark on More Data Mining with Weka, but you will certainly need equivalent knowledge.

In this second MOOC — even more than the first — you will do most of your learning in the Activities, and you should allow extra time for them because they’re a bit more challenging than before. Otherwise the format, and time commitment, is the same as the earlier course. Again, you do not have to complete the Activities to get a Statement of Completion: that’s based solely on your performance in the mid-class and end-of-class assessments.

There’s more information about the course in the trailer video: it’s informative, entertaining, and only about 3 minutes long.

A detailed syllabus is available.

By the time you have finished this course you will be an expert on the use of Weka. Enrol at:

Monday, 11 May 2015

Class 5 and final assessment available

The lessons for Class 5, the last in our course, are now available on the course website:

Class 5 broadens out to consider some more general issues. It's a short week, with just four topics: 
  • 5.1: The data mining process
  • 5.2: Pitfalls and pratfalls
  • 5.3: Data mining and ethics
  • 5.4: Summary
The post-course assessment is also now open. Everything will remain open until 25th May, when the course will be closed.
There is also a post-course survey for your opinions of the MOOC. 
We are also running a session of the follow-on MOOC "More Data Mining with Weka" starting 6th June. 

cheers, and enjoy the remainder of the course!


Monday, 4 May 2015

Class 4 is now available

The six lessons for Class 4 are now available on the course website:

This class introduces some more advanced methods and techniques. The topics are:
  • 4.1: Classification boundaries
  • 4.2: Linear regression
  • 4.3: Classification by regression
  • 4.4: Logistic regression
  • 4.5: Support vector machines
  • 4.6: Ensemble learning
The last three are high-performance contemporary algorithms. I aim to give you a conceptual understanding of what they do and how they work, but not the gory details. You have to learn to live and work in a world where you don't understand everything. You will see some mathematics in Lessons 4.2, 4.3 and 4.5. But don't worry: I'll explain it, and anyway you don't have to fully understand the math. 

Next week is the last. And it's short: Class 5 has only 4 lessons, not the usual 6. And it's more relaxed: no math at all.
cheers, and keep going!