I Built an Interactive Platform to Teach Machine Learning Differently

When I started learning and teaching machine learning, one thing kept bothering me.

Why is it so easy to write ML code, yet so difficult to understand what happens behind it?

You can train a linear regression model with a few lines of Python:

from sklearn.linear_model import LinearRegression

model = LinearRegression () model . fit ( X_train , y_train )

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But what exactly happens when we call fit() ?

How does the model determine its parameters? What role does mathematics play? And why do we need concepts like derivatives, matrices, and optimization?

These questions led me to rethink how machine learning could be taught.

Start with the problem, not the formula

Imagine predicting the price of a house.

We know its size is 1,500 square feet.…