I want to do more Kaggle courses again because I didn’t complete my 30-days-of-ml competition in August this year. I wonder why I didn’t finish it, as it was unquestionably something I could do. Maybe it was too easy.
This time, let’s pick some courses that I don’t know if I can do or not. I felt as I got older, doing things became synonymous with completing things. Like it’s important to complete things, I get it. But when it’s so far tilted to completion, I ended up not learning things because I didn’t think I would end up completing it. The entire activity becomes atelic.
Oliver Burkeman explained in his fantastic book, Four Thousand Weeks, that we’ve filled our lives with “telic activities”, activities whose values are derived from their telos, or ultimate aim. Learning is one of those atelic activities that have become thoroughly telic through mandatory schooling. When I went to school, I got good grades to gain recognition, not to learn anything new. It was challenging to change this mindset in adulthood because the stakes are higher. I learned things as an adult to get promoted, get more money, etc.
I think I might have a crack at becoming a life-long learner if I can view the act of learning as an activity itself. This way, it doesn’t matter how many courses I’ve done, how I do them and what I can do afterwards. I learn because learning is so damn fun. Because it feeds my soul.
So I looked through the list of courses that Kaggle offered. Some look easy, and some look hard. I picked Data Visualization and Feature Engineering. Since I’m a sucker for pretty charts, I know I’d enjoy the first one. I’d find the second one somewhat boring but probably quite useful going forward. I will skip all of the courses that I think I must do before doing Feature Engineering. I can always go back and pick those basic courses if I need to.
Let’s do some machine learning for the sake of learning and not for anything else.