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Regression Metrics Explained: MAE, RMSE, R², and Beyond
Master regression evaluation metrics like RMSE, MAE, R², and more. Learn how to measure model performance, compare metrics, and avoid common pitfalls in regression analysis.
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Generalized Linear Models: A Unified Framework for Modern Regression
A thorough guide to Generalized Linear Models (GLMs)—covering exponential family distributions, link functions, and models like logistic, Poisson, and negative binomial regression. Learn GLM structure, MLE, IRLS optimization, and model diagnostics for diverse response types.
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Beyond OLS — A Deep Dive into Ridge, Lasso, and Elastic Net
A complete guide to Ridge, Lasso, and Elastic Net regression—explaining their mathematical foundations, optimization techniques, and use cases. Learn how regularization addresses multicollinearity and overfitting, with visualizations, derivations, and practical model comparisons.
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Linear Regression Explained: From Normal Equations to Residual Diagnostics
A detailed guide to linear regression using OLS—covering the normal equation, Gauss-Markov assumptions, geometric intuition, and residual diagnostics like Q-Q plots, Cook’s distance, and DFBETAs. Learn to fit, interpret, and validate regression models with mathematical depth and practical insight.
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Data Preprocessing Part 6: Dimensionality Reduction
A comprehensive, hands-on guide to dimensionality reduction techniques for data scientists. Learn how to apply PCA, t-SNE, UMAP, autoencoders, and feature selection methods to simplify high-dimensional data, improve model performance, enhance visualization, and reduce computational cost—with clear math, Python examples, and practical best practices.