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Posted: 7/1/2021 7:17:31 PM EDT
I've been trying to get through the Coursera ML class and get to about week 5 and start having trouble. I get what linear regression is doing to get to local minima but am curious how you set things up to use ML in real world cases.
Any input would be much appreciated.
Link Posted: 7/1/2021 7:25:02 PM EDT
[#1]
I'm using libraries from R for routines like neural nets and random forests with some hyperparameter optimization and then comparing AUC to pick the best model.

Not terribly sophisticated ML but it works for what I'm doing which is rank ordering a list to select the best prospects.

It's rather static since it's getting refreshed once a month when IT doesn't fuck things up.
Link Posted: 7/1/2021 7:46:36 PM EDT
[#2]
Thanks @Mike_314! Had to look up what the AUC is.

Looks like this might be a good intro to some broader ML knowledge.
https://developers.google.com/machine-learning/crash-course
Link Posted: 7/1/2021 7:56:43 PM EDT
[#3]
This is a pretty thorough intro course https://www.coursera.org/learn/machine-learning#syllabus and Andrew Ng is a good instructor.
Link Posted: 7/1/2021 7:58:31 PM EDT
[#4]
Another R user here.

Probably not ML per se but I make heavy use of R's "forecast" function, which is a heavy handed way of throwing forecast spaghetti at the wall and seeing what sticks. Under the covers it tries a bunch of different models on the history it's fed to find the best one.

Unsurprisingly my work involves providing companies with forecasts.
Link Posted: 7/1/2021 8:17:06 PM EDT
[#5]
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Quoted:
This is a pretty thorough intro course https://www.coursera.org/learn/machine-learning#syllabus and Andrew Ng is a good instructor.
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Yeah... that's the one I'm taking.

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