I was discussing mortgage calculations with a friend today and realized this calculation would make an excellent interview question.
The problem is simple enough, but still requires some thought…
Writeup: interest-rate-calculations-using-recursion (PDF)
Source: interest-rate-calculations-using-recursion.js
If extra time, the candidate can be asked to write a solve function to solve for the payment P given the other values, e.g., solve for P in Zr(25*12,315000, 0.005,P) = 0.
Next week I’m interviewing for a Data Scientist position so I figured I better brush up my machine learning skills. I found some neat youtube tutorials [1,2] on using scikit-learn so I thought this would be a good place to start.
From experience, I was expecting that setting up the dev-environment with numpy, scipy, ipython notebook, etc, would take me half a day (compiling and debugging things that don’t work out of the box), but I was pleasantly surprised when a few pip commands later I had a fully functional environment. I’ve pasted the sequence of commands below for all those in case you want to learn yourself some ML too.
The first part is to create an isolated virtualenv for the project. Think of this as “basic python hygiene”: we want to isolate the python libraries used to follow the tutorial from my system-wide python library. (For most people this is just “best practices” but in my case my system-wide site-packages contains outdated versions, and or half-broken dependencies because of the dysfunctional relationship between fink, macports, and homebrew that plays out on my computer.) To setup a virtualenv in a given directory and activate it, proceed as follows:
$ cd ~/Projects/MLpractice
$ virtualenv pyML
$ . pyML/bin/activate # . is the same as source
Next we’ll install all the prerequisite packages and scikit-learn. Note that the command line starts with (pyML) which indicates that pip will install these packages in the pyML virtualenv and not system-wide.
(pyML)$ which python
(pyML)$ which pip
(pyML)$ pip install numpy
(pyML)$ pip install pyzmq
(pyML)$ pip install ipython[all]
(pyML)$ pip install scipy
(pyML)$ pip install pyparsing
$ brew update
$ brew install freetype
$ brew link --force freetype
$ brew install libpng
$ brew link --force libpng
$ brew install libagg
(pyML)$ pip install matplotlib
(pyML)$ pip install psutil
(pyML)$ pip install scikit-learn
Now everything is ready and setup for us.
We can clone the repositories with the example code and start the ipython notebook as follows.
$ git clone git@github.com:jakevdp/sklearn_scipy2013.git
$ git clone git@github.com:ogrisel/parallel_ml_tutorial.git
(pyML)$ cd sklearn_scipy2013/notebooks/
(pyML)$ ipython notebook --pylab inline
Your default browser should open showing you iPython notebooks for the first tutorial.
Let the learning begin—both for machine and human alike!
