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Ananddd06 / repository
🌟 Machine Learning In-depth Notes 📘 is an open-source collection of from-scratch implementations of Machine Learning algorithms, complete with research papers, Python scripts, and Jupyter notebooks. It covers both Supervised 🤖 and Unsupervised 🔍 learning methods, aiming to bridge the gap between theory and practical coding.
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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 ⚡.
This repo is a hands-on learning resource. Each algorithm is:
Our goal is to create a living library of ML knowledge — practical + theoretical.
Selected from shared topics, language and repository description—not editorial ratings.
The Ultimate Machine Learning Roadmap: From Scratch to Production. 📚 Chapter-by-chapter Jupyter Notebooks, Real-World Projects, and Deep Learning internals. 🤖 Master Scikit-Learn, TensorFlow, and PyTorch step-by-step. 🌟 Star to save your progress!
74/100 healtharyasingh258 /
Welcome to my Machine Learning Journey repository! 🌟 This is a personal project where I document my progress in learning Machine Learning (ML) from scratch. 📚 I regularly upload Jupyter notebooks covering various ML topics, ranging from basic concepts to advanced techniques. 🧠💻
27/100 healthMachine_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
Every algorithm includes its foundational research paper 📄.
Examples:
supervised/linear_regression/paper.pdf → Linear Regression theoryunsupervised/kmeans/paper.pdf → K-Means original paperThis way, you can connect code ↔ theory easily.
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
We 💙 contributions! You can:
add/naive_bayes)Hey there! 🙌
If you find this project helpful, please star ⭐ the repository and keep learning 📚✨.
With love ❤️, Anand
Faysoula /
A cozy home for all the AI adventures from class! filled with Jupyter notebooks and all sorts of python magic🌟tackling machine learning and data analysis concepts!!!
27/100 health