I have been making charts with R for almost as long as I have been using R, and with good reason: R is an amazing tool for filtering and visualizing data. With R, and particularly if we use the excellent ggplot2 library, we can go from raw data to compelling visualization in minutes.
One day, while I was walking around Cambridge, I had a random thought – how do the characters on the Simpsons feel about each other? It doesn’t take long to figure out how Homer feels about Flanders (hint: he doesn’t always like him), or how Burns feels about everyone, but how does Marge feel about Bart? How does Flanders feel about Homer? I then realized that I work with algorithms – maybe I would be able to devise one to answer this question. After all, I did something similar with the Wikileaks cables.
Update: you can find the next post in this series here.
Update: you can find the next post in this series here.
The determinant of a matrix is a number associated with a square (nxn) matrix. The determinant can tell us if columns are linearly correlated, if a system has any nonzero solutions, and if a matrix is invertible. See the wikipedia entry for more details on this.
Linear regression is a very basic technique that we use a lot in machine learning. In a lot of cases (and I have been guilty of this), we just use it without much thought as to how the internals actually work.
I had my natural predilection towards math crushed out of me at some point in school, and after that point, Math (yes, we are referring to the higher power of math) and I had a wary understanding. I dabbled quietly, and Math turned a blind eye to me ignoring some of its deeper theory. When I stuggled loudly, Math did its best to hide its smirks. I generally refrained from throwing textbooks.
This is the first, non-technical, part of this series. See the second part for more detail.
This is the second, technical, part of this series. See the first part for the overview.
I just gave a talk at Boston Python about natural language processing in general, and edX ease and discern in specific.