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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.

Has QUIT--Anony-Mousse
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user220201
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  • Even some "simple" ones are used since they can be a lot faster. Fast machine learning is nice when you have lots of data or time limitations. Practical use cases in general isn't very hard to find these days. – keyser Nov 23 '14 at 00:41
  • There are books written on this subject. Yes, all the methods that you mention are used in practice as well as in theory. – Gordon Linoff Nov 23 '14 at 00:42
  • I am really a novice and have no concept of "simple" or "complex" algorithms. I am really trying to just get a sense of how any of these are used. Can you suggest a few practical use cases? – user220201 Nov 23 '14 at 00:42
  • Movie recommendations/recommendations of any kind, scanning hand written text, scanning faces, spelling correction, diagnosis (of any kind), etc etc. And that's really just the tip of the iceberg – keyser Nov 23 '14 at 00:44
  • Which algorithms are used for these specific examples? The ones you have mentioned are enough for me if you can talk about the algorithms used for those. I will go inside the iceberg by myself :) – user220201 Nov 23 '14 at 00:47
  • All of them, some of them. No one algorithm fits any one problem. The algorithms are only blunt tools. The main difference at this level is supervised vs unsupervised, and classification vs regression. I'm no expert though. – keyser Nov 23 '14 at 00:49

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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)

m-bhole
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  • pkghosh link is for the whole blog, can you update it with the article you are talking about? – Darpan Nov 30 '16 at 00:05