Practical use cases for machine learning algorithms

Question

I am just starting out studying machine learning and currently doing Andrew Ng's course on Coursera. I am going through the course but am a bit lost. It will make studying all those algorithms/theory a lot rewarding if I can see some use cases for them.

For example, the first topic I read about was gradient descent and then linear regression and logistic regression. Are these used directly in practice or are other algorithms like k-means and kernel density used? I guess I am trying to get real world (software engineering, data mining) examples of these topics. Can some one suggest a post that might have some explanation of any machine learning algorithm(s) usage? It will be greatly helpful.

Answer 1

NO FREE LUNCH THEOREM states that if algorithm A outperforms algorithm B for some problem, then loosely speaking there must exist exactly as many other problems where B outperforms. So, it is difficult to link algorithm with particular use case.
If you are looking only for use cases where you can use machine learning algorithms, visit https://www.kaggle.com/wiki/DataScienceUseCases

Update : Just now, i came across http://pkghosh.wordpress.com Check it out. (use cases with algorithms)