Algorithm Cheatsheets

Welcome to the Algorithm Cheatsheets repository! This is a curated, ever-growing collection of quick-reference guides, summaries, and code snippets for core Machine Learning (ML) and Data Science (DS) algorithms.

Whether you're prepping for an interview, cramming for an exam, or just need a quick syntax refresher while modeling, this repo is built for you.

📌 Table of Contents

• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Data Preprocessing & Evaluation
• Contributing

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Supervised Learning

Algorithms that learn from labeled training data to predict outcomes.

• Linear Regression: Cheatsheet
• Logistic Regression: Cheatsheet
• Decision Trees & Random Forests: Cheatsheet
• Support Vector Machines (SVM): Cheatsheet
• K-Nearest Neighbors (KNN): Cheatsheet
• Naive Bayes: Cheatsheet
• Gradient Boosting (XGBoost, LightGBM): Cheatsheet

Unsupervised Learning

Algorithms that infer patterns, structures, and groupings from untagged data.

• K-Means Clustering: Cheatsheet
• Hierarchical Clustering: Cheatsheet
• Principal Component Analysis (PCA): Cheatsheet
• DBSCAN: Cheatsheet
• Association Rules (Apriori): Cheatsheet

Deep Learning

Neural networks and advanced architectures.

• Artificial Neural Networks (ANN): Cheatsheet
• Convolutional Neural Networks (CNN): Cheatsheet
• Recurrent Neural Networks (RNN & LSTM): Cheatsheet
• Transformers & Attention: Cheatsheet

Data Preprocessing & Evaluation

The essential foundations of building robust models.

• Feature Scaling (Normalization vs. Standardization): Cheatsheet
• Encoding Categorical Data: Cheatsheet
• Handling Missing Data: Cheatsheet
• Evaluation Metrics (Accuracy, F1, ROC-AUC, RMSE): Cheatsheet
• Cross-Validation & Hyperparameter Tuning: Cheatsheet

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How to Use This Repo

1. Browse: Navigate to the specific category you need from the table of contents.
2. Learn: Open the .md cheatsheet for a quick theoretical overview, pros/cons, assumptions, and key formulas.
3. Apply: Check the attached Python/Jupyter Notebook examples for standard scikit-learn, TensorFlow, or PyTorch implementations.
4. Bookmark: Star (⭐) this repository so you can easily find it during your next project or interview prep!

Contributing

We love contributions from the community! If you have a cheatsheet for an algorithm not listed here, or want to improve an existing one:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-cheatsheet).
3. Add your cheatsheet (Markdown or Jupyter Notebook).
4. Commit your changes (git commit -m 'Add KNN cheatsheet').
5. Push to the branch (git push origin feature/new-cheatsheet).
6. Open a Pull Request.

Please read our Contribution Guidelines for formatting rules.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details. Let's make learning open and accessible!

Made for Developers by Wecncode Dev Community 🩶