Machine Learning in a Year: From Total Noob to Effective Practitioner
Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.
By Per Harald Borgen, Xeneta.
This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject.
After this highly effective introduction, I continued learning on my spare time and almost exactly one year later I did my first ml project at work, which involved using various ml and natural language processing (nlp) techniques to qualify sales leads at Xeneta.
This felt like a blessing: getting paid to do something I normally did for fun!
It also ripped me out of the delusion that only people with masters degrees or Ph.D’s work with ml professionally.
The truth is you don’t need much maths to get started with machine learning, and you don’t need a degree to use it professionally.
In this post I want to share my journey, as it might inspire others to do the same.
First intro: Hacker News and Udacity
My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn’t even a professional developer, but a hobby coder who’d done a couple of small projects.
So I began watching the first few chapters of Udacity’s Supervised Learning course, while also reading all articles I came across on the subject.
This gave me a little bit of conceptual understanding, though no practical skills. I also didn’t finish it, as I rarely do with MOOC’s.
Failing Coursera’s ML Course
In January 2015 I joined the Founders and Coders (FAC) bootcamp in London in order to become a developer. A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. Every Tuesday evening, we’d watch lectures fromCoursera’s Machine Learning course.
It’s a fantastic course, and I learned a hell of a lot. But it’s tough for a beginner. I had to watch the lectures over and over again before grasping the concepts. The Octave coding task are challenging as well, especially if you don’t know Octave. As a result of the difficulty, one by one fell off the study group as the weeks passed. Eventually, I fell off it myself as well.
In hindsight, I should have started with a course that either used ml libraries for the coding tasks — as opposed to building the algorithms from scratch — or at least used a programming language I knew.
Learning a new language while also trying to code ml algorithms is too hard for a newbie.
If I could go back in time, I’d choose Udacity’s Intro to Machine Learning, as it’s easier and uses Python and Scikit Learn. This way, we would have gotten our hands dirty as soon as possible, gained confidence, and had more fun.
Lesson learned: Start with something easy and practical rather than difficult and theoretical.
Machine Learning in a Week
One of the last things I did at FAC was the ml week stunt. My goal was to be able to apply machine learning to actual problems at the end of the week, which I managed to do.
Throughout the week I did the following:
- got to know Scikit Learn
- tried ml on a real world dataset
- coded a linear regression algorithm from scratch (in Python)
- did a tiny bit of nlp
It’s by far the steepest ml learning curve I’ve ever experienced. Go ahead and read the article if you want a more detailed overview.
Lesson learned: Setting off a week solely to immerse yourself into a new subject is extremely effective.
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