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A comprehensive guide to applying statistical techniques in machine learning, including data preprocessing, model development, evaluation metrics, and real-world applications. This repository provides beginner-to-advanced insights into the statistical foundations of machine learning.

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Comprehensive Statistics Basics: A complete guide covering fundamental statistics concepts, techniques, and practical applications.

Statistics Basics

Welcome to the Statistics Basics repository! This repository contains a comprehensive summary of fundamental statistics concepts, techniques, and practical applications. Whether you're a beginner or looking to enhance your skills, this resource will guide you through essential statistical methods and tools.

πŸ’‘ Pro Tip: Star this repository to keep it handy for future reference!

πŸ“š Table of Contents

1. Introduction to Statistics

2. Descriptive Statistics

3. Probability

4. Inferential Statistics

5. Regression Analysis

6. Advanced Topics

7. Applications of Statistics

ℹ️ About

This repository is a comprehensive resource designed to serve as a learning tool and quick reference for anyone studying statistics. Each section contains clear explanations, practical examples, and interactive exercises.

❓ How to Use

Explore any topic in the Table of Contents to access detailed notes and examples. Each section is organized into Markdown files for easy navigation and customization.

🚩 Contributing

Contributions are welcome! If you have suggestions for enhancements or additional topics, feel free to open an issue or submit a pull request.

πŸ“ License

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

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A comprehensive guide to applying statistical techniques in machine learning, including data preprocessing, model development, evaluation metrics, and real-world applications. This repository provides beginner-to-advanced insights into the statistical foundations of machine learning.

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