https://github.com/pointer2alvee/machine-learning-algorithms-scratch
Core Machine Learning concepts , algorithms & maths implemented completely from scratch using Python & numpy only.
https://github.com/pointer2alvee/machine-learning-algorithms-scratch
machine-learning-algorithms numpy pandas probabilistic-machine-learning python statistical-models
Last synced: 5 months ago
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Core Machine Learning concepts , algorithms & maths implemented completely from scratch using Python & numpy only.
- Host: GitHub
- URL: https://github.com/pointer2alvee/machine-learning-algorithms-scratch
- Owner: pointer2Alvee
- Created: 2025-03-06T08:59:40.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T03:14:50.000Z (9 months ago)
- Last Synced: 2025-06-19T00:05:22.952Z (8 months ago)
- Topics: machine-learning-algorithms, numpy, pandas, probabilistic-machine-learning, python, statistical-models
- Language: Jupyter Notebook
- Homepage:
- Size: 646 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 📜 machine-learning-algorithms-scratch
#### 🧠 Overview
Comprehensive Machine Learning concepts & algorithms implemented from scratch using Python & numpy only.
This repository is a collection of core machine learning algorithms built without any external ML libraries like Scikit-learn or TensorFlow. The goal is to gain a deep understanding of how these algorithms work under the hood — mathematically and programmatically.
#### 🎯 Use Cases
- Implementing ML algorithms/models from scratch
- Learning the mathematical mehanism behind ML algorithms
- Learning the foundations of machine learning
- Teaching aid for ML courses
- Interview preparation
#### 🟢 Project Status
- Current Version: V1.0
#### 📃 Repository Structure
```
machine-learning-algorithms-scratch/
├── assets/
│ └── images/
├── datasets/
│ ├── various datasets
│ └── in (.csv/.json/.xlsx) format files
├── supervised/
│ ├── classification
│ │ ├── decision-tree
│ │ ├── knn
│ │ ├── logistic-regression
│ │ ├── naive-bayes
│ │ ├── random-forest
│ │ └── svm
│ └── regression/
│ │ ├── decision-tree
│ │ ├── linear-regression
│ │ └── random-forest
├── unsupervised/
│ │ ├── clustering
│ │ │ ├── kmc
│ │ ├── dimensionality-reduction
│ │ │ ├── pca
│ │ │ └── lda
│ │ ├── association-rule-learning
│ │ └── anomaly-detection
├── semi-supervised
├── README
└── .gitignore # gitignore file
```
### ✨ Features
- Covers supervised, unsupervised and semi-supervised ML algorithms
- Pure Python and NumPy — no ML libraries
- Modular, clean, and reusable code
- Educational and beginner-friendly
### 🚀 Getting Started
- Knowledge Required : python, linear algebra, probability, statistics, numpy, matplotlib
##### 💻 Software Requirements
- IDE (VS Code) or jupyter notebook or google colab
- Python 3
#### 🛡️ Tech Stack
- Python 💻
- Numpy, Pandas, Matplotlib, Scikit-Learn🧩
#### ⚙️ Installation
```
git clone https://github.com/yourusername/machine-learning-algorithms-scratch.git
cd ml-algorithms-scratch
```
#### 📖 Usage
- Open .ipynb files inside each algorithm directory and run them to see training/inference steps, plots, and results.
### 🧪 Algorithms Implemented
- ✅ Supervised-Classification :- Logistic-Regression, SVM, KNN, Naive-Bayes, Decision-Tree, Random-Forest
- ✅ Supervised-Regression :- LR, MLR, Decision-Tree, Random-Forest
- ✅ Unsupervised-Clustering :- KMC
- ✅ Unsupervised-DimenReduction :- PCA
- ⏳ Upcoming : LDA, Adaboost, SVM (SVR & non-linear SVM, kernel-trick), GBM, Neural-Network from scratch, Perceptron
### 🧭 Roadmap
- [x] Implement raw ML algorithms from scratch
- [ ] Re-implement ML algorithms using tensorflow
- [ ] Re-implement ML algorithms using scikit-learn
- [ ] Re-implement ML algorithms using pyTorch
### 🤝 Contributing
Contributions are welcomed!
1. Fork the repo.
2. Create a branch: ```git checkout -b feature/YourFeature```
3. Commit changes: ```git commit -m 'Add some feature'```
4. Push to branch: ```git push origin feature/YourFeature```
5. Open a Pull Request.
### 📜License
Distributed under the MIT License. See LICENSE.txt for more information.
### 🙏Acknowledgements
- Special thanks to the open-source community / youtube for tools and resources.