Practical guides to AI, software engineering, and reliable systems.
Build linear regression from first principles: vectors, projections, least squares, residual checks, and runnable Python with original mathematical visualizations.
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Check derivatives numerically, train a regression model step by step, and visualize how scaling, learning rates, and validation change the experiment.
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Understand base rates, implement Gaussian Naive Bayes in ordinary Python, and inspect log probabilities, decision boundaries, and held-out evaluation.
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