machine_learning: add numeric doctests to gradient_descent

[priya-sundaram-dev]

Continues the per-file doctest effort from #13919 (after #15285 for k_means_clust).

Describe your change:

Adds numeric doctests pinning the deterministic helper functions in machine_learning/gradient_descent.py on the module's fixed train_data/test_data and initial parameter_vector = [2, 4, 1, 5]:

• output — train/test lookup and the None fallback for an unknown data set
• _hypothesis_value / calculate_hypothesis_value — hypothesis value incl. bias term, plus the None fallback
• summation_of_cost_derivative and get_cost_derivative — bias (index=-1) and feature (index=0) cases

All values are computed from the file's own fixed data, so the tests are fully deterministic and don't touch the global-mutating run_gradient_descent. python -m doctest passes (12/12); ruff check and ruff format clean.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one issue, please mention Closes #{}.