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I suspect you have some small scraps of time during your workday in order to try to learn a subject that seems insurmountably large. The first suggestion I would make is to adopt a time management process, particularly the Pomodoro Technique, which allows you to break up large tasks into small ones and make the most of small, discrete chunks of time.

Second, you need to know what you will tackle in those small chunks of time. I’m sure there are other ways of breaking it up, but I might break learning test automation into these broad parts...

When looking for and evaluating candidates, I do not really care much about specific frameworks or technologies. These come and go, and their relative worth is generally due to either current fashion or their prevalence at whatever company happens to be offering the job. What I much prefer to see are skills and knowledge that are independent of the tools used.

As these roles and the expectations that surround them mature, it will become easier for companies to know what they are looking for, and easier to describe that in a job posting. With more examples to follow, candidates will have a clearer path to prepare for these jobs. Salaries will improve as the value of this work is made apparent, attracting more candidates, and we will see a healthier balance of supply and demand...

Automated testing is undergoing a huge transformation at WK ELM Solutions, and we need more creative and inspired people to make it happen! If you or anyone you know wants to be part of our revolution, check out this job posting for a Software Developer in Test.

Evan Estola from Meetup gave probably the second-best talk of the day (I have to give the best talk to Corinna Cortes) talking about our responsibility as wranglers of these learners to protect them from learning bigoted or otherwise harmful patterns. He noted that while some people believe that anything the learner infers from the data is somehow pure and bias-free, it is more accurate to say that while we are tinkering with every other constraint and parameter of a model we might as well fix the stuff that yields socially unwelcome inferences. He said it more elegantly of course, but it was neat that he kind of spanked everybody with that.

When Ben Hamner from Kaggle came up to the stage everyone in the room kind of straightened up and made little waking-up sounds, since Kaggle was recently acquired by Google, and because having the guy from Kaggle in a room full of ML people is a bit like having the guy from Easy Mac in a room full of undergraduate students. He didn't really get into that much in terms of the practice of machine learning, but did mention that winning learners are nearly always ensembles.

Periodically during the semester you will need to get updates from the class repo, so you have to maintain your connection to it. But you also really should be pushing your work somewhere, especially if you wind up getting accused of plagiarism--it's wise to have a paper trail, just in case. What you want is to be able to keep your connection to the class repo, but make it pull-only, and at the same time have a second connection to your private repo, where you can both push and pull to support a typical workflow.

One of the most important things to get right with my regexModeler application was the test suite, because I made several breaking changes many times along the way and would have been dead in the water without one. Because of all the random aspects of the program, it was an early challenge to get something for the unit tests to reliably measure. Now, if this is something you need to do too, bear in mind that object expressions can do much the same thing, but the limitation is that you need to define all of the methods for your solution to build. By taking the approach I outline below, you have more flexibility to define just the behavior you need to, while safely ignoring the rest. Also, if you want to take it a step further to add features similar to mocking libraries, you can do that.

Everything I read online suggested that to fix this situation I needed to build it from source, which meant installing compilers for C++ and Fortran, Cygwin, MinGW, and other stuff, none of which I actually want on that machine. There are Python distributions that already have SciPy working, but if you already have Python installed and are happy you may not want to mess with those. The answer for my situation wound up being that I had to dump the version of NumPy I was using and get the NumPy-MKL package instead, which contains the libraries mentioned in the above errors thanks to Intel's Math Kernel Library. So, here are the steps I eventually found to allow me to use SciPy with my existing Python environment without building it from source.

Our Visual Studio project contains a build step in which it copies files to a mapped drive, and we got funky behavior after the team upgraded to Windows 10. This behavior basically made it look like the destination wasn't there or that we didn't have access to it.