My own, very brief journey with AI and Machine Learning
Although my first attempt to embark on the long journey of learning AI and Machine Learning was cut short, I find that it’s worth sharing a few thoughts, so that others who want to jump in will know what to expect and hopefully be more prepared than I was.
For years I’ve been hearing about this new, up-and-coming wave that would sweep through the world and take away with it all those unprepared: the Artificial Intelligence. I didn’t want to be caught off-guard, so I decided to look into it deeper and try to even learn the basics of it, so that at the very worst I won’t be washed away, and at the very best, I could surf on top of it and better my career.
I decided to do some research and came across the online Machine Learning Certification Program offered by the reputable Stanford University and provided (free of charge) by Coursera. Taught by Andrew Ng, former head of Google Brain, among the topics included in the first week were Supervised and Unsupervised Learning, as well as best practices in Machine Learning.
All went well during the first week’s curriculum, and I even passed the first quizzes with flying colours. But when I started Week 2, I quickly bumped into a solid wall: programming. Now, to be fair, among the listed prerequisites for the Machine Learning course was a basic understanding of a programming language — which I had — but since this course was taught in MATLAB, my previous experience proved to be futile.
So I had to press the pause button on Machine Learning Week 2, and start looking into programming. This is how I came across Introduction to Programming with MATLAB, provided by Vanderbilt University (again, free of charge, through Coursera).
Same as before, the first (and second week this time) proved to be fairly easy to complete, especially since I had a background in mathematics and physics.
However, it soon became clear that a much deeper understanding of mathematics was required to be able to successfully complete this course.
It was at this point that I decided to make a few phone calls and inquire further about this discipline, asking colleagues of mine who were working in fields which were more closely related to/or part of AI and ML, such as Data Science and Data Mining. The consensus was that it required a deep understanding of mathematics and statistics, and just being curious about it without the serious commitment and dedication that only passion can ignite, it would only take a novice so far. In my case, through the end of Week 2…
In conclusion, if I was to give advice to someone thinking of getting to know the basics of AI/ML, I’d say make sure you do the exact opposite of what I did:
#1 Brush up on mathematics first and foremost. Make sure you have a deeper understanding than just what you remember from high school. Once you have a grasp on that, move to step 2.
#2 Complete the MATLAB curriculum, especially if you intend to take the ML course taught by Andrew Ng via Stanford University & Coursera. As this program is presented in MATLAB, you need to have a grasp on this programming language before moving on to step 3.
#3 If you can comfortably go through the first two steps, then (and only then) slowly ease into the Machine Learning field. It is now clear to me that unless you absolutely love statistics and math, this might not be the most enjoyable learning curve. Which is why the drop-out rate is so large.
