🧠 Machine Learning Algorithms

From Scratch & Scikit-Learn

A complete educational journey through classical Machine Learning — built from scratch, line by line.

Understand the math. Build the code. Train the mind.

⭐ Star this repo • 🍴 Fork it • 📚 Documentation • 🚀 Get Started

• 🎯 Overview
• ✨ Features
• 🏗️ Tech Stack
• 📂 Repository Structure
• 🚀 Quick Start
• 🧮 Algorithms Implemented
• 📊 Dataset Collection
• 📸 Visualizations
• 🎓 Learning Path
• 🤝 Contributing
• 👨‍💻 Author
• 📄 License
• ⭐ Support

Welcome to Machine Learning Algorithms — your comprehensive playground for mastering classical ML! This repository features hand-crafted implementations of every major algorithm, from the ground up.

• Learn by Building: Every algorithm implemented from scratch using pure NumPy
• Compare & Contrast: Side-by-side comparisons with industry-standard Scikit-Learn
• Visual Learning: Beautiful plots and visualizations that bring theory to life
• Real-World Applications: Applied examples on diverse datasets
• Educational Focus: Clear documentation, math explanations, and code comments

💡 Perfect for students, developers, data scientists, and AI enthusiasts who want to truly understand Machine Learning from first principles.

Category	Technologies
Language
Core Libraries
ML Framework
Visualization
Environment
Version Control

Machine-learning-Algorithm/
│
├── 📁 Supervised Learning
│   ├── 📂 Regression
│   │   ├── LinearRegression/          # Simple & Multiple Linear Regression
│   │   ├── PolynomialRegression/      # Polynomial Regression
│   │   └── GradientDescent/           # Batch, Mini-Batch, Stochastic GD
│   │
│   ├── 📂 Classification
│   │   ├── LogisticRegression/        # Binary & Multi-class Classification
│   │   ├── KNN/                       # K-Nearest Neighbors
│   │   ├── NaiveBayes/                # Gaussian, Multinomial, Bernoulli
│   │   ├── SupportVectorMachines/     # SVM with Kernel Tricks
│   │   ├── DecisionTrees/             # CART Algorithm
│   │   └── NeuralNetworks/            # Perceptron & MLP
│   │
│   └── 📂 Ensemble Methods
│       ├── RandomForest/              # Random Forest Classifier & Regressor
│       ├── Bagging/                   # Bootstrap Aggregating
│       ├── AdaBoost/                  # Adaptive Boosting
│       ├── GradientBoosting/          # Gradient Boosting Machines
│       └── XGBoost/                   # Extreme Gradient Boosting
│
├── 📁 Unsupervised Learning
│   ├── 📂 Clustering
│   │   ├── K-Means-clustering/        # K-Means from Scratch
│   │   ├── HierarchicalClustering/    # Agglomerative & Divisive
│   │   └── DBSCAN/                    # Density-Based Clustering
│   │
│   └── 📂 Dimensionality Reduction
│       └── PCA/                       # Principal Component Analysis
│
├── 📁 DataSets/                       # Curated Real-World Datasets
│   ├── iris.csv                       # Classification Dataset
│   ├── heart.csv                      # Healthcare Dataset
│   ├── Social_Network_Ads.csv         # Marketing Dataset
│   ├── ipl-matches.csv                # Sports Analytics
│   ├── zomato.csv                     # Restaurant Data
│   └── student_clustering.csv         # Educational Data
│
├── 📁 Visualizations/                 # Plots & Charts
├── 📄 requirements.txt                # Python Dependencies
├── 📄 CONTRIBUTING.md                 # Contribution Guidelines
├── 📄 LICENSE                         # MIT License
└── 📄 README.md                       # You are here!

• Python 3.9 or higher
• pip package manager
• Git

git clone https://github.com/Nitin-Prata/Machine-learning-Algorithm.git
cd Machine-learning-Algorithm

Windows (PowerShell)

python -m venv .venv
.\.venv\Scripts\Activate.ps1

macOS / Linux

python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt

jupyter notebook

Your browser will open automatically at http://localhost:8888

1. Navigate to any algorithm folder (e.g., LinearRegression/)
2. Open the Jupyter Notebook (.ipynb file)
3. Run cells sequentially to see the implementation
4. Experiment with parameters and datasets
5. Compare scratch implementation with Scikit-Learn

All datasets are curated, cleaned, and ready to use in the /DataSets folder.

1. Week 1-2: Linear & Logistic Regression

   • Start with LinearRegression/
   • Move to LogisticRegression/
   • Understand cost functions and gradient descent

2. Week 3: Classification Basics

   • Explore KNN/
   • Study NaiveBayes/
   • Practice on iris dataset

3. Week 4: Tree-Based Methods

   • Learn DecisionTrees/
   • Build intuition with visualizations

4. Week 5-6: Advanced Classification

   • Master SupportVectorMachines/
   • Understand kernel tricks
   • Implement NeuralNetworks/

5. Week 7: Ensemble Methods

   • Study RandomForest/
   • Compare with Bagging/
   • Understand bootstrap aggregating

6. Week 8: Clustering

   • Implement K-Means-clustering/
   • Explore HierarchicalClustering/
   • Try DBSCAN/

7. Week 9-10: Boosting Algorithms

   • Deep dive into AdaBoost/
   • Master GradientBoosting/
   • Optimize with XGBoost/

8. Week 11: Dimensionality Reduction

   • Understand PCA/
   • Apply to high-dimensional data

9. Week 12: Optimization Techniques

   • Compare gradient descent variants
   • Implement custom optimizers
   • Hyperparameter tuning

After completing this repository, you will:

• ✅ Understand the mathematical foundations of ML algorithms
• ✅ Implement algorithms from scratch using NumPy
• ✅ Debug and optimize ML code effectively
• ✅ Compare custom implementations with Scikit-Learn
• ✅ Visualize model behavior and decision boundaries
• ✅ Apply algorithms to real-world datasets
• ✅ Choose the right algorithm for specific problems
• ✅ Tune hyperparameters for optimal performance

Contributions are always welcome! Here's how you can help:

• 🐛 Report bugs and issues
• 💡 Suggest new algorithms to implement
• 📝 Improve documentation
• 🎨 Add visualizations
• 📊 Contribute new datasets
• ✨ Optimize existing code
• 🧪 Add unit tests

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please read CONTRIBUTING.md for detailed guidelines.

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 Nitin Pratap Singh

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files...

If you find this repository helpful, please consider:

Special thanks to:

• Andrew Ng for his legendary Machine Learning course that inspired this project
• CampusX for their exceptional ML tutorials and educational content
• Scikit-Learn team for the amazing library
• NumPy contributors for the numerical computing foundation
• The open-source community for inspiration
• You for taking the time to explore this repository!

• Pattern Recognition and Machine Learning by Christopher Bishop
• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
• Machine Learning Yearning by Andrew Ng

• Machine Learning by Stanford
• Fast.ai Practical Deep Learning
• StatQuest with Josh Starmer

• Scikit-Learn Documentation
• Kaggle Learn
• Machine Learning Mastery

Machine Learning is not just about using libraries — it's about understanding the principles that make those libraries work. This repository bridges the gap between theory and practice, empowering you to not just use ML, but to truly understand it.