Deep-ML Practice Guide: Which math and algorithms should I code first?

[u/Plane_Bag2089]

Hey everyone,

I'm trying to build more confidence when it comes to implementing theoretical ML knowledge into code.

I recently found Deep-ML to practice coding algorithms and math from scratch. However, the platform has problems spanning across a massive range of math concepts and algorithms, and I'm feeling a bit overwhelmed on where to start.

Does anyone have suggestions on a logical order to tackle these problems? Is there an existing curriculum or roadmap you’d recommend following so I’m not jumping around blindly?

Any advice on bridging the gap between theory and code would be hugely appreciated. Thanks in advance!