Machine Learning Exercises in Python, Part 1
Often this sort of material seems to be a collection of methods and understanding them, which is obviously important to being able to use them. However, I usually feel like the example problems are much cleaner and simpler than those I've encountered in business. I feel like there's this missing link between learning the methods and doing something that actually adds significant value for a business using machine learning. Perhaps it's just me or my field though.<p>I found that usually lots of work involved just transforming or examining data in relatively simple ways or using human expert decisions as to important threshholds for outliers. For example I could run an outlier algorithm on data and either the returned outliers were very obvious and could have been found using a manual query by knowing the business context, or it returned alot of false positive outliers that were useless for the business.
Other times, we'd have a predictive model that was good for 95% of cases but would make our company look ridiculous on predictions for the other 5%, so couldn't use it in production-- and the nature of the data was such that we couldn't use the model for only certain value ranges.<p>Perhaps it was just the nature of our realm of business (telecom), and these approaches are more useful for others (advertising, stock trading, etc). Any experience with business fields where this stuff made a sizable impact for something they productionized in business they can share?