I have finished the first part of the Coursera machine learning course I have started taking. In this part of the course by Andrew Ng, I learned about the history and purpose of machine learning, the difference between supervised and unsupervised learning, model and cost functions, and linear algebra.
I will start working on the next section of the machine learning course. This includes doing some actual programming assignments in the Octave or Matlab programming languages.
In addition, I have tried some different algorithms – ZeroR, DecisionStump, and M5P – on the Kaggle data sets, to see if any of them work better than IBk. However, these algorithms seem to have about the same error rate. As they are still a small sample, I will continue looking for new algorithms in the hopes of finding one that makes fewer errors.