I’m switching from Blockchain development to Machine Learning.
After working deeply in blockchain, I’ve decided to specialize in ML instead.
Not because blockchain is “dead” — but because ML in 2025 has more depth, demand, and long-term leverage.
For the next 60 days, I’ll follow a strict plan using only free resources:
-
Foundations
Auditing the PyTorch for Deep Learning Professional Certificate on DeepLearning.AI.
(No certificate. Just skills.) -
Real projects
Competing on Kaggle to build models that actually solve problems.
Rank doesn’t matter. Execution does. -
Public proof
Publishing every project on GitHub with clear READMEs: problem, approach, results. -
Deployment
Turning models into simple apps or APIs so others can use them.
I’ll share progress, mistakes, and results along the way.
If you’ve transitioned between specializations, what was the hardest part?
