Aspiring AI Engineer Builds Linear Regression from Scratch Using NumPy

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A developer on a self-described 280-day journey to becoming an AI/ML engineer spent Day 4 implementing linear regression using Python's NumPy library rather than relying on high-level frameworks like scikit-learn. The exercise focused on understanding the underlying mechanics, including how data is vectorized into arrays and how a model updates weights and biases through gradient descent. The learner drew connections between core mathematical concepts — linear algebra and calculus — and their practical application in code. The experience reinforced the value of a strong mathematical foundation before moving on to abstraction-heavy tools. The post also invited experienced developers to share whether they similarly built algorithms from scratch before adopting frameworks like PyTorch or scikit-learn.

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