## 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.

**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.