My Machine Learning Adventure
This is Day 1. I have been skeptical about this. I wasn’t sure if I can do this. I was having doubts here and there so much so that it disrupted my daily schedule, from researching relevant information to postponing school work. But I have made up my mind. I’m going in. Wish me luck.
I am going to dive into the world of Machine Learning and AI, knowing what suits me best. Oh, I’m a Mechatronics Engineering student, so it makes sense to learn AI and machine learning.
But no. Having a clear goal in mind will tell people that pursuing this especially new field in technology is going to be promising, it’s going to change some field in some community. I have read books on how AI and ML can change the world in an unimaginable way and it fascinates me. But no, I don’t have a clear goal yet. I do know some of the uses of AI and ML and how they are capable of solving a ton of problems, and setting a clear goal in mind will be my quest for the next few months, alongside my ML adventure. Stick with me and see what I will be doing with ML, but first, this will be a documentation of my learning adventure for God knows how long it will take. I’m excited for this. I’m going in. Wish me luck.
Day 1
This should be Day 1. Yesterday was Day 0. I didn’t know anything at all.
I have been watching some tutorial videos on Coursera and I was starting to wonder if I made the right decision. Again and again I would ask myself, “Do I really have to go through this? I could just get a normal job and work my ass off for the next 20 years or so. Artificial Intelligence probably is not my field.” These questions came circling my head the entire day, and then upon scrolling through the news, I stumbled upon the fact that DeepMind’s AI has beaten world-class gamers in a recent video stream. Starcraft to be exact.
That got me into it again. AI will never leave my life, it’s either I jump onto the bandwagon now or I’ll be dragged by one.
So I continued with some video tutorials and I wouldn’t say the starting is smooth, unlike any other skills, the learning curve of this field is an exponential one. My math at this stage is OK, I understood almost everything in the first few tutorials, but I quickly learnt that the implementations of the methods using different libraries like Pandas, NumPy, Matplotlib are quite new to me. In order to know how to write ML codes effectively, I guess I need to learn some basics of these libraries. This might not be the right way to learn, but I’m on full sponge-mode now.
Day 2
I try not to dwell too much on the code but try to absorb all the information and concepts being used in the process because that’s what’s important. The concepts are not too bad to me. At least for now.
I moved on to read the codes. I freaked out. I didn’t understand which function did what and I was in a mess. At least I felt so. I decided to take a break and go for a walk. Other stuff started to flood my mind and I just knew I had to resist. I had to resist quitting. I calmed myself down, searched up a few articles on how to study Machine Learning again, but this time, more thoroughly. I found that the functions in the codes are from many of the libraries out there. I made a plan. I am going to sit down and study what each function means and how each one works, so I can apply them easily.
**********Few hours later**********
It’s about 10pm now. I just finished going through the lines of code and they make much more sense to me now. This is the idea. Learning has to be in a detailed manner, with or without supervision, it is our own responsibility to get things right. Calm down, make a plan, stick to it until you get something out of it. Day 3 tomorrow.
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