Machine Learning progress update
Early in September, I started taking a course on learning. Essentially it is a course on how to be a better learner. Learning about learning might sound silly, but it was a great course with many great strategies to employ when trying to master new material or acquire new skills.
As part of the course, we had to pick a project that we will use to apply the techniques we were learning. The concept made a lot of sense. In my experience, the best way to learn a practical skill is to combine the theoretical knowledge with practical work, so the project seemed very appropriate.
My project was to take and finish a course on Machine Learning. I knew close to nothing in this area, and it is a field that is hot in software engineering. ML being new to me, it gave me a chance to program using techniques that are completely unknown and that makes things a bit frustrating but also a lot of fun.
The course I went with was Udacity’s Intro to Machine Learning course. So far the intuition to pick that course is proving to be correct. I finished it ahead of my planned scheduled. It took me just a tad over two months, while mostly studying on weekends and occasional early morning.
The most fun part was applying the skills learned from the course at my current job. We do some video processing tasks and such things have been notoriously tricky to estimate how long they will take to complete. With ML, and more specifically regression analysis, it was a breeze to build a model that gave excellent predictions on processing durations. Some of the predicted times were within seconds of the actual times, most within minutes, which was more than sufficient when you consider the processing could last anywhere from 20 to 40 minutes (with some outliers of course shorter or much longer).
My goal was to apply the techniques in some capacity by February 2017, and being able to do that so much earlier was a big boost and motivator to continue going strong. Actually one of the learning course main preaching points was to use what is being learned right away, even if you don’t feel like you know what you are doing. It just strengthens the knowledge and right away deepens your grasp of the concepts that you are learning.
I am highly recommending udacity’s course for the others that might like to start ML journey themselves. It is not very heavy on theory, although one should use the topics presented to dive into more theory online. The examples and mini projects they present are really great, interesting and informative. If you know a bit of python you are pretty much ready to go. Knowing some of the advanced math conceps helps to understand the course better, but it is not necessary in order to use the techniques.
What’s next? I am happy to share that I got accepted to AI nano degree. It does not start until February 2017, so in the meantime, I am taking another ML intro course, this time Stanford’s Machine Learning course which I debated to take before picking Udacity’s option. Stanford course is great, but presents much more theory and is a bit more “drier”, more pedantic. I am on a week 3 now of the course, going ahead as far as I can before Neural Network weeks. That area might prove to be very complicated so it will be good to have as much cushion as possible for quizzes and learning.
That’s it for now. I can’t wait to see where this ML journey will bring me. Hopefully, I will continue to deepen and strengthen my practical skills and start applying it in everyday life with regularity.