Update machine learning documentation for clarity and completeness

docs/src/machine-learning.md

@@ -1,11 +1,17 @@
 # Machine Learning
 
-RustFrame ships with several algorithms:
+The `compute::models` module bundles several learning algorithms that operate on
+`Matrix` structures. These examples highlight the basic training and prediction
+APIs. For more end‑to‑end walkthroughs see the examples directory in the
+repository.
+
+Currently implemented models include:
 
 - Linear and logistic regression
-- K-means clustering
+- K‑means clustering
 - Principal component analysis (PCA)
-- Naive Bayes and dense neural networks
+- Gaussian Naive Bayes
+- Dense neural networks
 
 ## Linear Regression
 
@@ -37,3 +43,34 @@ let cluster = model.predict(&new_point)[0];
 
 For helper functions and upcoming modules, visit the
 [utilities](./utilities.md) section.
+
+## Logistic Regression
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::logreg::LogReg;
+use rustframe::matrix::Matrix;
+
+let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
+let mut model = LogReg::new(1);
+model.fit(&x, &y, 0.1, 200);
+let preds = model.predict_proba(&x);
+assert_eq!(preds.rows(), 4);
+```
+
+## Principal Component Analysis
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::pca::PCA;
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let pca = PCA::fit(&data, 1, 0);
+let transformed = pca.transform(&data);
+assert_eq!(transformed.cols(), 1);
+```
+
+For helper functions and upcoming modules, visit the
+[utilities](./utilities.md) section.