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o this is a quick one off. I’m actually working through the MS Virtual Academy Machine Learning course (and will probably go after the certificate). One of the things I am still have problems remembering is the various (common) types of Machine Learning. These things actually build an algorithm to resolve a particular type of problem.

Machine Learning-Types

04 April 2016
Jay Kimble

So this is a quick one off. I’m actually working through the MS Virtual Academy Machine Learning course (and will probably go after the certificate). One of the things I am still have problems remembering is the various (common) types of Machine Learning. These things actually build an algorithm to resolve a particular type of problem. It’s pretty cool because you provide it with a set of data and it does something with that data. So without further adieu, here is my notes on these types:

Classification: This ultimately answers a yes/no question or true/false assertion. So if you want to predict the answer to a yes/no question. For instance, you might want to know if the Cleveland Browns win the super bowl this year; Classification will predict this (if you need ML to learn this, then you haven’t been paying attention.. the answer in Spring of 2016 is a definite NO).

Regression: This is similar to the last one except you want to predict some outcome. For instance, how many points will the Cleveland Browns score this year (you are probably seeing a pattern here)? BTW, this number will probably be a low number.. possibly less than 100..

Clustering: This groups data together. Using different types of clustering will net different types of results. Let’s say you plugged in NFL football teams and put in there record each year over say the last 15 years as well as the number of points scored, super bowls won, etc. You could then have it group these teams into groups. You can tell it how many groupings you want, etc. (of course the Browns would be grouping of losing teams). You could also plug in team colors, and the team logo as opposed to win loss stats, and the machine learning might group teams by color and maybe even by whether the mascot is an animal or something else. (Clustering can interpret images)

Recommender Systems: Historically this is the one we know the most about. With e-commerce systems we’ve wanted to predict what other products a user might want to buy. This is exactly what this type of machine learning does. (another question might be, “after following the Browns for 35 year, which NFL team should I switch to that feels the most like my team (but with a winning record?” Ok, maybe I’m not ready for that question).

  ML