Dev.to / Medium / Substack
I Finished Machine Learning. And Then Changed The Plan.
A few months ago, I had no idea what feature engineering was.
Today, I finished my Machine Learning roadmap.
Not "mastered ML."
Not "became an AI expert."
Finished the part where every tutorial starts making sense.
I built projects, broke models, overfit them, leaked data into them, fixed them, and slowly started understanding why things worked instead of blindly following notebooks.
My latest project:
Customer Churn Prediction
Predicting which customers are likely to leave a company before they actually do.
Built with:
- Python
- Pandas
- Scikit-Learn
- XGBoost
- Feature Engineering
- Hyperparameter Tuning
Project:
Customer Churn Prediction Demo
The funny thing?
The more I learned, the less interested I became in rushing toward Deep Learning.
Originally the plan was:
ML → Deep Learning → NLP
But somewhere along the way I realized something.
I don't just want to understand models.
I want to build products.
Things people actually use.
So the roadmap changed.
Now I'm diving into:
- Generative AI
- RAG
- LangChain
- FastAPI
- Ollama
- MCP
- LangGraph
- Agentic AI
Deep Learning isn't gone.
It's just waiting its turn.
And DSA?
That was supposed to stay consistent.
Instead, I keep finding myself opening AI documentation at 2 AM and disappearing into another rabbit hole.
Not because I have to.
Because I genuinely can't stop.
Somewhere between building projects and studying, curiosity quietly took over.
So that's where we are now.
Machine Learning: complete.
Next stop: GenAI.
Let's see how deep this rabbit hole goes.
If you're earlier in your journey:
Build projects before you feel ready.
Most of what I learned came from fixing mistakes I didn't know I was making.
Project:
Customer Churn Prediction Demo