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A collection of machine learning algorithms in Python, including supervised, unsupervised, reinforcement learning, and deep learning, with Jupyter notebooks.

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Machine Learning Algorithms

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🎯 A collection of machine learning algorithms implemented in Python, including Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Deep Learning.

πŸ“Œ Table of Contents

  1. Why This Repository?
  2. Features
  3. Algorithm Categories
  4. Algorithm Comparison
  5. Getting Started
  6. Contributing
  7. Support
  8. License
  9. Connect with Me

🧐 Why This Repository?

Machine learning is a vast field, and while there are numerous frameworks and libraries available, beginners often struggle to understand the fundamental algorithms that power these frameworks. This repository was created to provide:

  • Clear, well-structured implementations of popular ML algorithms.
  • Minimal dependencies, making it easier to experiment and learn without complex setups.
  • Categorized ML techniques, covering different learning paradigms for structured understanding.
  • A foundation for further exploration, enabling users to modify, extend, and integrate these algorithms into their own projects.

Whether you are a beginner looking to grasp the basics or an experienced developer wanting quick reference implementations, this repository aims to be a practical, educational, and accessible resource.

πŸ”₯ If you find this repo useful, please consider ⭐ starring it! It helps others discover it.


πŸš€ Features

βœ… Well-documented implementations of ML algorithms
βœ… Categorized structure for easy navigation
βœ… Minimal dependencies for quick setup
βœ… Open-source & beginner-friendly


πŸ“‚ Algorithm Categories

πŸ”΅ Supervised Learning

🟑 Unsupervised Learning

🟒 Reinforcement Learning

πŸ”΄ Deep Learning


πŸ“Š Algorithm Comparison

Algorithm Type Strengths Weaknesses
Linear Regression Supervised Simple, interpretable, fast Sensitive to outliers
Decision Tree Supervised Easy to interpret, non-linear relationships Prone to overfitting
K-Means Clustering Unsupervised Fast and scalable Assumes spherical clusters
PCA Unsupervised Reduces dimensionality, speeds up computation Loses some interpretability
Q-Learning Reinforcement Learns optimal policies Can be computationally expensive
Deep Q-Network (DQN) Reinforcement Handles large state spaces well Requires a lot of training data
Simple Neural Network Deep Learning Learns complex patterns Requires a lot of data
Convolutional Neural Network (CNN) Deep Learning Excellent for image processing Computationally intensive

β–Ά Getting Started

πŸ”§ Prerequisites

Ensure Python is installed. Install dependencies using:

pip install -r requirements.txt

β–Ά Running an Algorithm

Navigate to the respective directory and execute a Python script:

python SupervisedLearning/LinearRegression.py

πŸ”— Try It in Google Colab

Click below to run these algorithms in Google Colab without installing anything:
Open in Colab


πŸ›  Contributing

We welcome contributions! Here’s how you can help:

  1. Fork the repository.
  2. Create a branch (feature-new-algorithm).
  3. Commit your changes.
  4. Submit a Pull Request (PR).

πŸ” Check open issues to find something to work on!


⭐ Support

If you find this project useful:

  • Star the repo ⭐ (top right corner)
  • Share it on social media
  • Suggest improvements in the Issues tab

πŸ“œ License

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


πŸ“’ Connect with Me

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