I started machine learning by doing some tutorial. I learned a lot, the best one for me was the Sentdex book.

But tutorials have so much limitations, like how do I get the data ? how to handle 100GB of data ? kaggle makes all this so simple while it is hard in reality.

I was in tutorial hell for few months so I decided to make a ML project from scratch.

The idea: An AI that detect balding from a picture of a side profile.

It looks doable, no one has done it and I am curious if I am balding myself. There is something called the “Norwood scale”, it’s a scale from 0 to 7.

0 means perfect hairline and 7 means fully bald.

Step 1: Get the data

In tutorials, the data is given to us so it was a first. It’s really hard to find enough quality data but after some time on internet I found tens of thousand of images of men with side profile.

I started to label them from 0 to 7 (The Norwood scale). Data annotation started to make me crazy, I did it for few hours the first day.

I understand after some times that I would need to make a small ap