Data Science Cheatsheets

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What are Cheatsheets?

Cheatsheets are concise reference guides that provide quick access to essential information, commands, or concepts. They are especially useful in data science and programming, where remembering numerous functions, parameters, and best practices can be challenging.

Machine Learning Cheatsheets

• Scikit-learn Cheatsheet

  Quick reference for Scikit-learn, a Python library that provides simple and efficient tools for data analysis and machine learning, including classification, regression, clustering, and dimensionality reduction.

• NumPy Cheatsheet

  Quick reference for Numpy, a powerful library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions.

• Statistical tests in Python cheatsheet

  Quick reference for Python statistical analysis guide covering t-tests, ANOVA, chi-square, correlation, regression, and non-parametric tests with code examples.

• Machine Learning Algorithms Cheatsheet

  Quick reference for Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, KNN, K-Means Clustering, DBScan, and Hierarchical Clustering.

• PyCaret Cheatsheet

  A comprehensive guide to PyCaret, covering setup, data preprocessing, model training, evaluation, deployment, and advanced features for streamlined machine learning workflows.

• XGBoost Cheatsheet

  Quick reference for XGBoost, a popular gradient boosting library

• CatBoost Cheatsheet

  Essential information about CatBoost, a gradient boosting library with built-in categorical feature support

• Dimensionality Reduction Cheatsheet

  Dimensionality reduction using UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) transforms high-dimensional data into lower dimensions, facilitating visualization and revealing underlying patterns by preserving local and global data structures, respectively

• Principal Component Analysis Cheatsheet

  PCA is a dimensionality reduction technique that finds the directions of maximum variance in high-dimensional data and projects it onto a lower-dimensional subspace.

• Pytorch Cheatsheet

  Quick reference for Pytorch, one of the top deep learning libraries written in Python

Large Language Models

• 🤗 Hugging Face Hub Client Library Cheatsheet

  Quick reference for the Hugging Face Hub Client Library, a central place to discover and share ML models, datasets, and Spaces.

• Fine-Tuning LLMs using Gradient Package

  Comprehensive guide on fine-tuning large language models using the Gradient package, covering setup, data preparation, and fine-tuning process.

• PEFT (Parameter-Efficient Fine-Tuning) Cheatsheet

  Quick reference for PEFT methodologies, including LoRA, IA3, and adapter methods for efficient model fine-tuning.

• Fabric Cheatsheet

  Fabric is an excellent open-source framework designed to seamlessly integrate AI into your everyday life and work. It empowers you to leverage the power of large language models (LLMs) by providing a user-friendly interface and a vast library of reusable AI prompts called Patterns.

• Fabric Patterns Cheatsheet

  Quick reference for Fabric Patterns, providing a comprehensive guide to various patterns used in the Fabric framework for efficient AI integration.

• LangChain Expression Language (LCEL) Cheatsheet

  A comprehensive guide to LCEL, covering composition primitives, patterns, and best practices for building efficient LangChain applications.

Generative AI Agents

• OpenAI Agents SDK Cheatsheet

  A comprehensive guide to the OpenAI Agents SDK, covering core concepts, agent creation, tools, guardrails, handoffs, and tracing for building powerful AI agent applications.

DevOps

• Docker Cheatsheet

  A comprehensive guide to Docker essentials, covering image management, container lifecycle, information retrieval, networking, data persistence, and Docker Compose for efficient containerization workflows.

Databases

• SQLite Cheatsheet

  Quick reference for SQLite database operations, including table management, data manipulation, queries, indexes, transactions, and importing/exporting data.

Aider Cheatsheet

• Aider Cheatsheet

  Quick reference for Aider, an AI-powered coding assistant that helps you write, refactor, and debug code.

Diagrams as Code

Revolutionize Your Technical Documentation

Diagrams as Code: Exploring Mermaid, PlantUML, D2 and Generating Diagrams using AI LLMs

By Omar Kamal Hosney

★★★★★ 4.8 out of 5 stars (7 ratings)

In today's age of rapid technological advancement, the boundaries between the written word and code are becoming increasingly blurred. Dive into a revolutionary approach to content generation, where traditional documentation meets the principles of 'code'.

Unravel the transformative strategies of "document as a code" and "diagram as a code". Discover how lightweight markup languages have ushered in a new era where documents evolve from mere static pages to dynamic, version-controlled entities.

Survey Our Introductory Guide!

• Master ASCIIdoc for effortless document creation
• Decode the world of Different Software Charts
• Bring presentations to life with Marpit Slides
• Dive underwater with Mermaid charts and graphics
• Understand the magic of Declarative Diagram (D2)
• Watch your designs grow with PlantUML Markdown Language
• Step into the future with our Generative AI Pipeline chapter

Current feature: Learn document creation with ASCIIdoc

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