🌟 Machine Learning In-depth Notes

Welcome to Machine Learning In-depth Notes 📘✨ — an open-source collection of from-scratch implementations of Machine Learning algorithms.
This project covers Supervised 🤖 and Unsupervised 🔍 learning methods, each paired with its research paper PDF 📄.
Our mission is to learn by building, understanding ML from the ground up, and making it accessible to everyone!

We’re not stopping here 🚦 — coming soon: NLP 📝, Deep Learning 🧠, and Transformers ⚡.

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📑 Table of Contents

• 🔎 Overview
• ⚙️ Implemented Algorithms
• 📂 Directory Structure
• 📖 Research Papers
• 💻 How to Use
• 🤝 Contribute
• 🚀 Future Roadmap
• 📜 License

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🔎 Overview

This repo is a hands-on learning resource. Each algorithm is:

• ✅ Implemented from scratch (no shortcuts!)
• 📄 Documented with the original research paper
• 📓 Accompanied by Python scripts & Jupyter Notebooks for demos

Our goal is to create a living library of ML knowledge — practical + theoretical.

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⚙️ Implemented Algorithms

🎯 Supervised Learning

• 📈 Linear Regression
• 📉 Logistic Regression
• ⚡ Support Vector Machine (SVM)
• 🌳 Decision Tree
• 🌲 Random Forest
• 🔜 (more coming soon!)

🔍 Unsupervised Learning

• 🔑 K-Means Clustering
• 🏗 Hierarchical Clustering
• 📊 Principal Component Analysis (PCA)
• 🔜 (more coming soon!)

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📂 Directory Structure

Machine_Learning_Indepth_Notes/
├── supervised/
│   ├── linear_regression/
│   │   ├── linear_regression.py
│   │   ├── linear_regression.ipynb
│   │   └── paper.pdf
│   └── logistic_regression/
│       ├── logistic_regression.py
│       ├── logistic_regression.ipynb
│       └── paper.pdf
├── unsupervised/
│   ├── kmeans/
│   │   ├── kmeans.py
│   │   ├── kmeans.ipynb
│   │   └── paper.pdf
│   └── pca/
│       ├── pca.py
│       ├── pca.ipynb
│       └── paper.pdf
├── README.md
└── LICENSE

📖 Research Papers

Every algorithm includes its foundational research paper 📄.
Examples:

• supervised/linear_regression/paper.pdf → Linear Regression theory
• unsupervised/kmeans/paper.pdf → K-Means original paper

This way, you can connect code ↔ theory easily.

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💻 How to Use

Clone the repo and dive in!

git clone https://github.com/Ananddd06/Machine_Learning_indepth_notes.git
cd Machine_Learning_indepth_notes

To run any program

cd supervised/linear_regression
python linear_regression.py

👉 Make sure you install dependencies first:

pip install -r requirements.txt

🤝 Contribute

We 💙 contributions! You can:

• ➕ Add new algorithms
• 📓 Improve Jupyter notebooks with visualizations
• 📄 Upload missing research papers
• 🛠 Refactor / optimize existing code
• ✨ Suggest new features

Steps to contribute:

1. 🍴 Fork this repo
2. 🌿 Create a branch (e.g., add/naive_bayes)
3. 💾 Commit changes
4. 📬 Open a Pull Request

🚀 Future Roadmap

• Supervised Learning algorithms
• Unsupervised Learning algorithms
• NLP implementations (from scratch) 📝
• Deep Learning basics 🧠
• Transformers ⚡

⭐ Star My Repo

Hey there! 🙌

If you find this project helpful, please star ⭐ the repository and keep learning 📚✨.

With love ❤️, Anand