Day 4/280 of my journey to becoming an AI/ML Engineer.

A developer learning AI/ML engineering documented building linear regression from scratch using NumPy rather than relying on scikit-learn's model.fit(), implementing vectorized arrays for linear algebra and gradient descent to update weights and biases. The engineer framed the exercise as a way to connect mathematical foundations like matrix multiplication and derivatives to practical code, and asked experienced developers whether they built algorithms from scratch before adopting frameworks such as PyTorch or scikit-learn.

Day 4/280 of my journey to becoming an AI/ML Engineer. 🚀 Today was all about bridging the gap between theoretical mathematics and practical code. I spent the day deep-diving into Python’s NumPy library to understand the mechanics of Linear Regression. While it’s tempting to just import scikit-learn and run model.fit , I really wanted to understand what is happening under the hood. Seeing the concepts I’ve studied over the last few days come together in code was incredibly rewarding. The data is vectorized into NumPy arrays Linear Algebra , and the model updates its weights and biases by calculating the error and stepping down the slope Calculus & Gradient Descent . It was a powerful reminder of why a strong mathematical foundation is so critical. When you understand matrix multiplication and derivatives, Python stops feeling like a magic spell and starts feeling like a highly precise tool. To the experienced developers out there: when you were first learning, did you build ML algorithms from scratch using pure NumPy before moving on to libraries like PyTorch or scikit-learn? Or did you jump straight into the frameworks?