Machine Learning Repository

🌐 Overview

This repository is a curated collection of machine learning concepts, algorithms, and practical implementations.
It serves as both a learning resource and a showcase of applied machine learning techniques.
The content is organized into modular sections, each focusing on a specific area of machine learning.

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📁 Repository Structure

The repository is structured into the following directories:

• 1. Data Preprocessing: Techniques for cleaning, transforming, and preparing data for modeling.
• 2. Regression: Implementation of regression algorithms and analysis.
• 3. Classification: Classification models and their applications.
• 4. Clustering: Unsupervised learning methods for grouping data.
• 5. Association Rule Learning: Discovering interesting relations between variables in large datasets.
• 6. Reinforcement Learning: Algorithms that learn optimal actions through rewards and penalties.
• 7. Natural Language Processing (NLP): Techniques for processing and analyzing textual data.
• 8. Deep Learning: Neural network architectures and deep learning models.
• 9. Dimensionality Reduction: Methods for reducing the number of variables under consideration.
• 10. Model Selection and Booting: Strategies for selecting the best model and improving performance.

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🚀 Getting Started

To explore the notebooks and code:

1. Clone the repository:

   git clone https://github.com/adheesh101/Machine-Learning.git
   

2. Navigate to the desired directory:

   cd Machine-Learning/2. Regression
   

3. Open the Jupyter Notebook:

   jupyter notebook
   

🤝 Contribution

Contributions are welcome!
If you have suggestions, improvements, or new content to add, feel free to fork the repository and submit a pull request.

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📜 License

This project is licensed under the MIT License.

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Crafted with curiosity and code by Adheesh Soni.