Day 3 was about slowing down and understanding the math behind ml and implementing it.....
I focused on implementing linear regression from scratch using mathematical intuition rather than jumping straight into libraries. I explored how predictions are formed, how parameters affect the line, and started digging into cost functions — I get the idea at a high level, but I still need to deeply understand what exactly they represent and why they work. That’s on tomorrow’s list.
Alongside ML, I pushed an improvement to my portfolio on GitHub, refining layout and structure as I continue building it incrementally.
I didn’t get to DSA planning today as intended, but instead of forcing it, I’m scheduling it intentionally for tomorrow. Learning consistency > pretending everything went perfectly.
What I worked on today:
ML: Implemented linear regression using math (I studied about regression as a part of college math, it helped me implement this code) and Explored the intuition behind cost functions
Wanna check the code out?Portfolio: insted of click added a scroll property so that the transition can be made even smooth. more coming soon!!!
Progress Log
What’s next?
- Properly break down cost functions
- Plan and start DSA practice
- Keep building — even if things feel unfinished
Learning in public. One step at a time.
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