Add user guide examples

docs/src/machine-learning.md

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+# Machine Learning
+
+RustFrame ships with several algorithms:
+
+- Linear and logistic regression
+- K-means clustering
+- Principal component analysis (PCA)
+- Naive Bayes and dense neural networks
+
+## Linear Regression
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::linreg::LinReg;
+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![2.0, 3.0, 4.0, 5.0], 4, 1);
+let mut model = LinReg::new(1);
+model.fit(&x, &y, 0.01, 100);
+let preds = model.predict(&x);
+assert_eq!(preds.rows(), 4);
+```
+
+## K-means Walkthrough
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::k_means::KMeans;
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
+let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
+let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
+let cluster = model.predict(&new_point)[0];
+```
+
+For helper functions and upcoming modules, visit the
+[utilities](./utilities.md) section.