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https://github.com/sanikamal/machine-learning-atoz

Beginner-friendly machine learning tutorials and mini-projects.
https://github.com/sanikamal/machine-learning-atoz

collaborative-filtering data-analysis data-visualization decision-trees kmeans-clustering knn machine-learning machine-learning-algorithms recommender-system regression svm

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Beginner-friendly machine learning tutorials and mini-projects.

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# Machine Learning A to Z 🌟🚀

A collection of awesome, beginner-friendly machine learning tutorials and projects. This repository provides hands-on examples, comprehensive explanations, and essential techniques for machine learning practitioners. Ideal for those starting in the field and looking to expand their knowledge through practical implementation.

## Contents 📚

| Title | Description | Technology/Library | Link | Article |
|-------------|----------------|---------------------|-------|---------|
| 5 Machine Learning Libraries | Overview of the 5 machine learning libraries in Python for fast and efficient development. | `NumPy`, `Pandas`, `Matplotlib`,`NLTK`, `Scikit-learn` | [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/notebook/top_five_machine_learning_libraries_in_python.ipynb) | Python Libraries for ML |
| Simple Linear Regression | Implement simple linear regression to predict engine size on fuel consumption dataset. | `Pandas`, `Matplotlib`, `Scikit-learn` | [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/Simple-Linear-Regression.ipynb) | Basics of Linear Regression |
| Spam Detection Using Naive-Bayes| Detect spam emails using Naive-Bayes classification techniques. | `Scikit-learn`,`Pandas`| [View Notebook](https://github.com/sanikamal/machine-learning-atoz/blob/master/spam-detection/spam-detection.ipynb) | Spam Detection with Naive-Bayes |

## Contribution Guidelines

We welcome contributions to this project! Please follow the standard [contribution guidelines](CONTRIBUTING.md) to submit pull requests or raise issues.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.