Contribute to ML-Personal-Notes: Build a Study Resource for Future Aspirants

[Soumilgit]

Hello everyone,

I’ve started ML-Personal-Notes as a growing, open resource for anyone interested in Machine Learning.
The idea is simple: make ML concepts accessible, rigorous, and practical - all in one place.

Each topic is written as a self-contained note (Markdown/PDF) combining:

• Mathematical foundations (derivations, equations, intuition)
• Practical perspectives (implementation insights, real-world context)
• Architecture breakdowns (structured explanation of models/algorithms)
• Diagrams (visuals for better retention)

Currently, the repo includes notes on:

• Transformer models
• MCP server architecture
• A few classical ML models & algorithms (Decision Trees, KNN, Naive Bayes, Random Forest, SVM, XGBoost, SMOTE, etc.)

But this is just the beginning. The vision is to create a collaborative, topic-wise library that helps students, developers, and enthusiasts not only study ML but also develop curiosity and deeper interest in it.

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How You Can Contribute

I welcome contributions in the form of:

• New topic notes (Markdown or PDF).
• Additional derivations or alternative proofs.
• Practical insights linking math to implementation.
• Architecture diagrams, flowcharts, or visuals.

Contribution standards:

1. Keep everything in one file per topic (math + practice + architecture + diagrams).

2. Use Markdown for editable notes; PDFs are great for polished versions.

3. Write math clearly (LaTeX formatting preferred).

4. Keep content concise, structured, and approachable.

5. Follow the flow:

   • Introduction
   • Mathematical derivations
   • Practical perspective
   • Architecture overview
   • Diagrams
   • References (optional)

A ready-to-use template is provided in the repo: template-topic.md

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Goal

The aim is to grow this into a go-to reference for ML learners - something that explains both how models work under the hood and how they are used in practice.

If you are passionate about mathematics, ML, or just explaining concepts clearly, your contributions will help future aspirants learn and stay curious about ML.

Let’s build this together.

• Soumil

[Soumilgit]

Repo link: https://github.com/Soumilgit/ML-Personal-Notes .
Oh, and yes, do star it for a star swap⭐ .
Fork it for contributing - and you get a follow-back 🤝 .
Let's make this open-source repository VIRAL and fulfilling needs of many - including us 🙏 !