Machine Learning on Coursera

Recently I’ve finished Machine Learning course on Coursera and I think I can recommend it to anyone who wants to start doing something in this field. This area is not something new to me since my specialization on master’s degree was Intelligent Information Systems which covered this area pretty well. By doing this course I wanted to get another point of view on that. Now I can definitely say that prof. Andrew Ng does a good job explaining the material presented on the course. He starts with cost function, gradient descent which are the foundation of Machine Learning. Then he explains such topics as:

  • classification (with multiclass classifications),
  • logistic regression,
  • neural networks with backpropagation,
  • bias and variance,
  • SVM,
  • clustering,
  • anomaly detection,
  • photo OCR.

Everything is presented with real file examples. Students are required to write some code with the elements explained in each part in Matlab or Octave which are supported by scripts that simplify the way of submitting answers. If there is a one thing I could complain about it would be the out of date scripts that for newer version of Octaves require some patches which can be easily found on course’s wiki.