feat: Integrate MLE core algorithm implementations

[tsvlgd]

This PR integrates a foundational collection of machine learning algorithm implementations, primarily focusing on from-scratch approaches for educational purposes. This directly supports the repo's mission of providing beginner-friendly resources by explaining the core mechanics of models.

The implementations are organized into the newly created implementations/ directory.

• Content Added:
  • Regression: From-scratch Linear and Logistic Regression.
  • Linear Models: Perceptron and Adaline implementations.
  • Trees & Ensembles: Decision Trees and Scikit-learn based ensemble examples.
  • Dimensionality Reduction: PCA and SVD implementations.
• Context: This PR is the first major content addition following the structural reorganization merged in the previous PR.

Type of change

• Added a new machine learning frameworks, libraries or software.
• Documentation update

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings

[github-actions]

Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊

[tsvlgd]

Hi @sanjay-kv,

This PR follows our plan for sequencing contributions.

This is the Implementations PR (PR 2), which adds the core algorithm notebooks (linear models, trees, etc.) into the new implementations/ directory, established by the previous restructure (PR #1672).

It is ready to merge immediately after PR #1672 is complete.