Today, I explored the foundations of Machine Learning, focusing on the difference between supervised and unsupervised learning. Understanding when and why each approach is used helped clear up a lot of confusion I had earlier around ML being just “models and math.”
I also got into the basics of Linear Regression — what the model is trying to learn, how it fits data, and why it’s often the starting point for supervised learning. I’m deliberately keeping things simple right now and focusing on intuition before diving deeper.
On the building side, I documented my third meaningful push to GitHub for my portfolio website. The project is still evolving, but pushing code felt important — not to show perfection, but to start building a visible trail of progress.
What I worked on today:
ML: Supervised vs Unsupervised Learning and Linear Regression fundamentals
Portfolio: UI updates & structure updates
commit history
This phase is all about understanding before optimizing and building in public without waiting to feel “ready”.
Back at it tomorrow.
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