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The goal is to gain a deep understanding of how these algorithms work under the hood — mathematically and programmatically.\n\n#### 🎯 Use Cases \n- Implementing ML algorithms/models from scratch\n- Learning the mathematical mehanism behind ML algorithms\n- Learning the foundations of machine learning\n- Teaching aid for ML courses\n- Interview preparation\n  \n#### 🟢 Project Status\n- Current Version: V1.0\n\n#### 📃 Repository Structure\n```\nmachine-learning-algorithms-scratch/\n├── assets/\n│   └── images/\n├── datasets/\n│   ├── various datasets\n│   └── in (.csv/.json/.xlsx) format files\n├── supervised/\n│   ├── classification\n│   │   ├── decision-tree\n│   │   ├── knn\n│   │   ├── logistic-regression\n│   │   ├── naive-bayes\n│   │   ├── random-forest\n│   │   └── svm\n│   └── regression/\n│   │   ├── decision-tree\n│   │   ├── linear-regression\n│   │   └── random-forest\n├── unsupervised/\n│   │   ├── clustering\n│   │   │   ├── kmc\n│   │   ├── dimensionality-reduction\n│   │   │   ├── pca\n│   │   │   └── lda\n│   │   ├── association-rule-learning\n│   │   └── anomaly-detection\n├── semi-supervised\n├── README\n└── .gitignore                  # gitignore file\n```\n\n### ✨ Features\n- Covers supervised, unsupervised and semi-supervised ML algorithms\n- Pure Python and NumPy — no ML libraries\n- Modular, clean, and reusable code\n- Educational and beginner-friendly\n\n\u003c!-- ### 🔍 Demo\n\u003ca href=\"https://youtu.be/Qor8kjsCJkA?si=7d1Mhc0KW4GQb3sF\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.youtube.com/vi/Qor8kjsCJkA/hqdefault.jpg\" alt=\"YouTube Video\" width=\"390\" height=\"270\"\u003e\n\u003c/a\u003e --\u003e\n\n### 🚀 Getting Started\n- Knowledge Required : python, linear algebra, probability, statistics, numpy, matplotlib\n\n\u003c!-- ### 🛠️ Hardware Requirements\n- None\n--\u003e\n\n##### 💻 Software Requirements\n- IDE (VS Code) or jupyter notebook or google colab\n- Python 3\n  \n#### 🛡️ Tech Stack\n- Python 💻\n- Numpy, Pandas, Matplotlib, Scikit-Learn🧩\n\n\u003c!--\n### 🖇️ Schematic\n- none\n--\u003e\n\n#### ⚙️ Installation\n```\ngit clone https://github.com/yourusername/machine-learning-algorithms-scratch.git\ncd ml-algorithms-scratch \n```\n\n#### 📖 Usage\n- Open .ipynb files inside each algorithm directory and run them to see training/inference steps, plots, and results.\n\n### 🧪 Algorithms Implemented\n- ✅ Supervised-Classification   :- Logistic-Regression, SVM, KNN, Naive-Bayes, Decision-Tree, Random-Forest\n- ✅ Supervised-Regression       :- LR, MLR, Decision-Tree, Random-Forest\n- ✅ Unsupervised-Clustering     :- KMC\n- ✅ Unsupervised-DimenReduction :- PCA\n\n- ⏳ Upcoming  : LDA, Adaboost, SVM (SVR \u0026 non-linear SVM, kernel-trick), GBM, Neural-Network from scratch, Perceptron\n\n### 🧭 Roadmap\n- [x] Implement raw ML algorithms from scratch\n- [ ] Re-implement ML algorithms using tensorflow\n- [ ] Re-implement ML algorithms using scikit-learn\n- [ ] Re-implement ML algorithms using pyTorch\n\n### 🤝 Contributing\nContributions are welcomed!\n1. Fork the repo. \n2. Create a branch: ```git checkout -b feature/YourFeature```\n3. Commit changes: ```git commit -m 'Add some feature'```\n4. Push to branch: ```git push origin feature/YourFeature```\n5. Open a Pull Request.\n\n### 📜License\nDistributed under the MIT License. See LICENSE.txt for more information.\n\n### 🙏Acknowledgements\n- Special thanks to the open-source community / youtube for tools and resources.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointer2alvee%2Fmachine-learning-algorithms-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpointer2alvee%2Fmachine-learning-algorithms-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointer2alvee%2Fmachine-learning-algorithms-scratch/lists"}