Machine Learning Algorithms

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

📌 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

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🧐 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.

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

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🚀 Features

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

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📂 Algorithm Categories

🔵 Supervised Learning

• LinearRegression.py - Linear Regression
• DecisionTree.py - Decision Tree

🟡 Unsupervised Learning

• KMeansClustering.py - K-Means Clustering
• PCA.py - Principal Component Analysis (PCA)

🟢 Reinforcement Learning

• QLearning.py - Q-Learning
• DQN.py - Deep Q-Network (DQN)

🔴 Deep Learning

• Neural_Network.py - Simple Neural Network
• CNN.py - Convolutional Neural Network (CNN)

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📊 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

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▶ Getting Started

🔧 Prerequisites

Ensure Python is installed. Install dependencies using:

pip install -r requirements.txt

▶ Running an Algorithm

Navigate to the respective directory and execute a Python script:

python SupervisedLearning/LinearRegression.py

🔗 Try It in Google Colab

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

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🛠 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).

🔍 Check open issues to find something to work on!

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⭐ Support

If you find this project useful:

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

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📜 License

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

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📢 Connect with Me