Add t-SNE implementation and tests for dimensionality reduction

[Komil-parmar]

Implemented the t-distributed stochastic neighbor embedding (t-SNE) algorithm in dimensionality_reduction.py, including input validation and a test function.

Describe your change:

This PR adds a complete implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to the dimensionality reduction module.

What's Added
Function: t_distributed_stochastic_neighbor_embedding
Algorithm: Non-linear dimensionality reduction technique for data visualization

Features:
Perplexity-based probability computation with binary search optimization
Student-t distribution for low-dimensional mapping
Gradient descent with momentum for optimization
Comprehensive input validation and error handling

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• 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.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• 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 or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".