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.

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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.

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Project:
Customer Churn Prediction Demo