Finished Machine Learning Course

I had couple of ML-related courses at University and used it in practice, but, still it was very interesting to review and refresh the core concepts.

Especially from such a badass ML expert as Andrew Ng, with lots of practical examples and advices.

My Notes and the Course by Andrew Ng.

Machine Learning Notes

  • Supervised / Unsupervised Learning.
  • Linear Regression, Logistic Regression, SVM.
  • Neural Networks.
  • K-means, PCA, Anomaly Detection.
  • Recommending Systems, Collaborative Filtering.
  • Bias / Variance, Regularization, Precision / Recall, Learning Curve etc.
  • How to gather, amplify and generate Learning Data.
  • Large-scale Learning, Stochastic Gradient, Map/Reduce etc.

Also learn core topics about NLP - Natural Language Processing: TF/IDF, String Similarity, etc.

And, refreshed my rusty memory of Linear Algebra and Statistics from University.

Really like that course, lots of examples how to apply those technics to real cases.