This GitHub repository gathers the most popular cheatsheets and quick reference guides for Artificial Intelligence (AI) and Machine learning (ML).

For an ease of download and browse over the files, a Google Drive version of this GitHub repository is available here.

The global structure of this GitHub repository follows to some extent the following AI and Machine Learning roadmap.

• 01- Mathematics
  
• 02- C++
  
• 03- Python
  
• 04- Computer architecture
  
• 05- Data structures
  
• 06- Automata theory
  
• 06- Complexity theory
  
• 07- SQL
  
• 08- Data cleaning
  
• 09- Data visualization
  
• 10- Mathematical logic
  
• 11- Introduction to AI
  
• 12- Machine learning
  
• 13- Deep learning
  
• 14- Metrics to evaluate ML algorithms
  
• 15- Reinforcement learning
  
• 16- Time series
  
• 17- Git
  

• 01- Mathematics
  
  • Calculus cheat sheet all reduced
  • Calculus cheat sheet
  • Linear Algebra in 4 pages
  • Probability cheat sheet
  • Probability distribution cheat sheet
  • Statistics cheat sheet
  • Super-cheatsheet-mathematics
• 02- C++
  
  • C++ Reference Card
  • C++ Libraries
  • C++ OOP Reference Card
• 03- Python
  
  • Python for Beginners
  • Python Reference Cheatsheet
  • Python cheatsheet
  • Python for Data Science cheatsheet
  • Numpy cheatsheet
  • Pandas cheatsheet 1
  • Pandas cheatsheet 2
  • Matplotlib cheatsheet 1
  • Matplotlib cheatsheet 2
  • Scikit-Learn cheatsheet
  • List, Tuples, Sets, Dictionary
  • Tutorial Python
• 04- Computer architecture
  
  • Computer organisation cheatsheet
• 05- Data structures
  
  • Classification of Data structures
  • Data structures
  • Complexity
  • Resources
• 06- Automata theory
  
  • Languages and Automata cheathseet
  • Automata cheatsheet
  • Context-Free Grammar cheatsheet
• 06- Complexity theory
  
  • Complexity Theory Cheat Sheet
  • Computability Theory Cheat Sheet
• 07- SQL
  
  • SQL-quick-guide
  • SQL operations
  • SQL commands
  • SQL-basics-cheat-sheet-a4
  • SQL joins-cheat-sheet-a4
  • Study-guide-data-retrieval-with-SQL
  • SQL Roadmap
• 08- Data cleaning
  
  • Data-cleaning-checklist
  • Data-cleaning-guide
  • Data-preparation-cheatsheet
  • Feature engineering
  • Feature-selection-methods
• 09- Data visualization
  
  • Core principles of Data Visualization
  • Visual Vocabulary
  • Data visualization cheatsheet
  • The chart chooser
• 10- Mathematical logic
  
  • cheatsheet-logic-models
• 11- Introduction to AI
  
  • cheatsheet-states-models
  • cheatsheet-variables-models
• 12- Machine learning
  
  • Machine learning process
  • Machine-learning-map
  • Machine learning algorithms
  • How to choose a ML algorithm 1
  • How to choose a ML algorithm 2
  • Comparison of ML algorithms 1
  • Comparison of ML algorithms 2
  • Comparison of ML algorithms 3
  • Comparison of ML algorithms 4
  • Comparison of ML algorithms 5
  • super-cheatsheet-machine-learning
  • Machine learning cheatsheets
  • Machine learning explainability
  • Machine learning operations MLOps
• 13- Deep learning
  
  • super-cheatsheet-deep-learning
  • Large Language Models cheatsheet
  • main types of neural networks
  • Architecture - Classification MLP
  • Architecture - Regression MLP
  • Activation Function - Hidden Layer
  • Activation Function - Output Layer
• 14- Metrics to evaluate ML algorithms
  
  • Metrics-machine-learning
  • Performance-measure-machine-learning
• 15- Reinforcement learning
  
  • Reinforcement learning cheatsheet 1
  • Reinforcement learning cheatsheet 2
• 16- Time series
  
  • Time-series-cheat-sheet
• 17- Git
  
  • Git-cheat-sheet
  • Git-cheat-sheet 2