Machine Learning Cheatsheets

Welcome to the Machine Learning Cheatsheets repository! This collection of cheatsheets is designed to help you quickly reference key concepts, algorithms, and libraries in the field of Machine Learning. Whether you're a beginner looking for a quick overview or an experienced practitioner in need of a refresher, these cheatsheets can be a valuable resource.

Cheatsheets

Below are the cheatsheets currently available in this repository:

1. Scikit-Learn Cheatsheet: A comprehensive reference for Scikit-Learn, a popular Machine Learning library in Python.

2. TensorFlow Cheatsheet: A quick reference for TensorFlow, a powerful open-source Machine Learning framework.

3. PyTorch Cheatsheet: A cheatsheet for PyTorch, a popular deep learning framework.

4. Matplotlib Cheatsheet: Useful for creating data visualizations in Machine Learning projects.

5. NumPy Cheatsheet: A fundamental library for scientific computing in Python, often used in Machine Learning.

6. Pandas Cheatsheet: Helpful for data manipulation and analysis, commonly used in data preprocessing for Machine Learning.

7. Keras Cheatsheet: Useful for high-level neural network tasks, often used in conjunction with TensorFlow.

8. Machine Learning Algorithms Cheat Sheet: A comprehensive cheat sheet of Machine Learning algorithms.

9. Machine Learning Math Cheat Sheet: A cheat sheet covering essential math concepts for Machine Learning.

10. Machine Learning Interview Cheat Sheet: A cheat sheet to help you prepare for Machine Learning interviews.

11. Linear Regression Cheat Sheet: A comprehensive cheat sheet for Linear Regression.

12. Logistic Regression Cheat Sheet: A reference for Logistic Regression with examples.

13. Decision Tree Cheat Sheet: An overview of Decision Trees and their terminology.

14. Support Vector Machine Cheat Sheet: A cheat sheet for Support Vector Machines (SVM).

15. Random Forest Cheat Sheet: An overview of Random Forests and their usage.

16. Gradient Boosting Machine Cheat Sheet: A reference for Gradient Boosting Machines.

17. K-Means Clustering Cheat Sheet: A cheat sheet for K-Means Clustering.

18. Hierarchical Clustering Cheat Sheet: An overview of Hierarchical Clustering.

19. Principal Component Analysis Cheat Sheet: A reference for Principal Component Analysis (PCA).

20. Singular Value Decomposition Cheat Sheet: An overview of Singular Value Decomposition (SVD).

21. T-Distributed Stochastic Neighbor Embedding Cheat Sheet: A cheat sheet for t-SNE.

To learn more, check out the ML Cheat Sheets folder in the main branch.

Contributing

We encourage contributions to expand the collection of cheatsheets. If you'd like to add more cheatsheets to this repository, follow these steps:

1. Fork this repository.

2. Create a new markdown (.md) or Jupyter Notebook (.ipynb) file for the new cheatsheet in the appropriate category within the Cheatsheets directory.

3. Ensure that the cheatsheet is well-structured and includes clear explanations, code snippets, and relevant references.

4. Update this README.md file to include a link to the new cheatsheet in the "Cheatsheets" section.

5. Submit a pull request with your changes, and we'll review and merge it.

Please make sure to provide proper attribution and adhere to any licensing requirements if you use content from external sources.

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License

This repository is licensed under the MIT License, which means you are free to use, modify, and distribute the content for both personal and commercial purposes while providing attribution.

We hope you find these cheatsheets helpful in your Machine Learning journey. Feel free to reach out with suggestions, feedback, or requests for new cheatsheets. Happy learning!