Implement Machine Learning Algorithms From Scratch (NumPy Only)

10 weeks · 0 milestones

Implement logistic regression, a decision tree, k-means clustering, and a feedforward neural network from scratch using NumPy only — no PyTorch, TensorFlow, or sklearn for the core algorithm logic. Each implementation must be tested on a held-out dataset and benchmarked against the equivalent sklearn implementation on the same data, with documented results explaining any performance differences. The from-scratch implementation is what demonstrates understanding: it is possible to use sklearn without understanding gradient descent; it is not possible to implement gradient descent in NumPy without understanding it. Proof: the implementations reviewed by an ML engineer or CS researcher who provides a different dataset and asks you to predict which of your algorithms will perform best and why — you must reason from your implementation, not just run it.

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