Machine-Learning-Algorithms-Collection

Machine Learning Algorithms Collection

93+ Machine Learning & Deep Learning Algorithms with Python Implementations, Comments, Sample Inputs/Outputs, Visualizations, and Mini Projects.

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About This Repository

This repository contains a complete collection of Machine Learning, Deep Learning, and Artificial Intelligence algorithms implemented using Python.

The main goal of this repository is to help:

• 📚 Beginners learn ML easily
• 💻 Students prepare for placements
• 🚀 Developers build strong GitHub portfolios
• 🤖 AI enthusiasts understand algorithms with code

Each algorithm includes:

✅ Beginner-Friendly Code
✅ Detailed Comments
✅ Sample Input & Output
✅ Visualization (where required)
✅ Explanation of Logic
✅ Mini Projects
✅ Real Dataset Examples

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Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• TensorFlow
• Keras
• OpenCV
• PyTorch

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Repository Structure

Machine-Learning-Algorithms-Collection/
│
├── Supervised Learning/
├── Unsupervised Learning/
├── Deep Learning/
├── Reinforcement Learning/
├── NLP/
├── Computer Vision/
├── Time Series/
├── Recommendation Systems/
├── Anomaly Detection/
├── Clustering/
├── Regression/
├── Classification/
└── README.md

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Machine Learning Algorithms Included

Regression Algorithms

• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Logistic Regression

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Classification Algorithms

• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• XGBoost
• LightGBM
• CatBoost
• AdaBoost

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Clustering Algorithms

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Mean Shift
• Gaussian Mixture Model (GMM)
• Spectral Clustering

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Dimensionality Reduction

• PCA
• t-SNE
• UMAP

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Deep Learning Algorithms

• Artificial Neural Network (ANN)
• Perceptron
• Multilayer Perceptron (MLP)
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Long Short-Term Memory (LSTM)
• GRU
• Transformer
• GAN
• Autoencoder

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Anomaly Detection

• Isolation Forest
• Local Outlier Factor (LOF)
• One-Class SVM

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Association Rule Learning

• Apriori
• FP-Growth
• Eclat

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Reinforcement Learning

• Q-Learning
• SARSA
• Deep Q Network (DQN)

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NLP Algorithms

• Word2Vec
• FastText
• BERT
• GPT
• Seq2Seq
• Transformer

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Computer Vision

• CNN
• ResNet
• YOLO
• RCNN
• U-Net
• Vision Transformer (ViT)

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Features

✨ Clean and Organized Code
✨ Easy for Beginners
✨ Placement Preparation Friendly
✨ GitHub Portfolio Ready
✨ Real-World Use Cases
✨ Interview-Oriented Examples
✨ Step-by-Step Learning

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Sample Algorithm Format

# Import Libraries
import numpy as np

# Sample Data
X = np.array([1, 2, 3, 4, 5])

# Print Data
print("Input:", X)

# Output
print("Output:", X * 2)

Output

Input: [1 2 3 4 5]
Output: [ 2  4  6  8 10]

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Installation

Clone the repository:

git clone https://github.com/snehalathaArakkonam/Machine-Learning-Algorithms-Collection.git

Move into the folder:

cd Machine-Learning-Algorithms-Collection

Install dependencies:

pip install -r requirements.txt

Run Python files:

python filename.py

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Future Improvements

• Add Web Applications
• Add Streamlit Deployments
• Add Real Industry Datasets
• Add AI Projects
• Add Interview Questions
• Add Mathematical Explanations
• Add Notes PDFs

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Contributions

Contributions are welcome!

If you'd like to improve algorithms, add projects, or fix issues:

1. Fork the repository
2. Create a new branch
3. Commit changes
4. Create a Pull Request

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Support

If you found this repository useful:

Star this repository
Fork this repository
Share with others

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Author

Snehalatha Arakkonam

• GitHub: https://github.com/snehalathaArakkonam

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License

This project is licensed under the MIT License.

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Made with ❤️ for Beginners, Students, and Future AI Engineers 🚀