Flatten project folder structure and fix notebook links in READMEs

linear-models/README.md

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+# Linear Models — From Normal Equations to Logistic Regression
+
+A from-scratch deep dive into linear models, building each method from its mathematical foundation before confirming the result against scikit-learn.
+
+## Overview
+
+Rather than just calling `.fit()`, this notebook implements the underlying math directly (normal equation, gradient descent) to build intuition for what scikit-learn's estimators are actually doing internally.
+
+## Covered
+
+1. **Linear regression via the normal equation** — solving `θ = (XᵀX)⁻¹Xᵀy` directly, then verifying the result matches scikit-learn's `LinearRegression`.
+2. **Batch gradient descent** — implemented manually, with cost-function convergence visualized over iterations.
+3. **Stochastic gradient descent** — compared against batch GD for convergence speed and stability, then via scikit-learn's `SGDRegressor`.
+4. **Polynomial regression** — fitting non-linear (quadratic) data by engineering polynomial features.
+5. **Bias-variance tradeoff** — learning curves showing underfitting vs overfitting as model complexity changes.
+6. **Regularization** — Ridge, Lasso, and ElasticNet compared, with a visualization of how each shrinks coefficients differently as the penalty strength increases.
+7. **Logistic regression** — binary classification via the sigmoid function, with a visualized decision boundary.
+8. **Softmax regression** — multiclass classification on the Iris dataset, with visualized decision regions across all three species.
+
+## Tech stack
+
+Python, NumPy, scikit-learn, matplotlib
+
+## Notebooks
+
+- [`linear_models_notebook.ipynb`](linear_models_notebook.ipynb) — original notebook, my own work
+- [`linear_models_notebook_polished.ipynb`](linear_models_notebook_polished.ipynb) — same analysis with AI-assisted documentation and formatting added for readability