A comprehensive collection of quick references, implementations, and guides for artificial intelligence, machine learning, and data science technologies. This repository serves as a structured learning path from fundamental concepts to advanced AI applications.
Core Python and numerical computing foundations:
Tools for data manipulation and analysis:
Mathematical and scientific computation libraries:
- SciPy - Scientific algorithms and tools
Libraries for creating plots, charts, and interactive visualizations:
- Matplotlib - Fundamental plotting library
Classical machine learning algorithms and libraries:
- Scikit-learn - Comprehensive ML library
- XGBoost - Gradient boosting framework
- LightGBM - Fast gradient boosting
- Supervised Learning - Classification and regression algorithms
- Linear/Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes
- Unsupervised Learning - Clustering and dimensionality reduction
- Reinforcement Learning - RL algorithms and techniques
Neural networks, frameworks, and architectures:
- Frameworks - PyTorch, TensorFlow, JAX, Lightning
- Architectures - Neural network architectures and implementations
- CNN, RNN/LSTM/GRU, Feedforward, ResNet, GANs
- Optimization - Training techniques and optimization
Image processing and computer vision techniques:
- OpenCV - Computer vision library
- YOLO - Object detection and real-time applications
- Vision Transformer - Transformer-based image classification
- U-Net - Semantic segmentation architecture
- CLIP - Contrastive Language-Image Pre-training
- Diffusion Models - Text-to-image generation and creative AI
Text processing and language model tools:
- NLTK - Natural Language Toolkit for text processing
- spaCy - Industrial-strength NLP library
- Transformers - BERT, GPT, T5 and modern transformer architectures
End-to-end implementations and real-world applications:
- Image Classification - Complete computer vision pipeline
- Sentiment Analysis - NLP sentiment classification project
- Recommendation System - Collaborative filtering and content-based recommendations
- Chatbot - Conversational AI system
- Classification - General classification projects and techniques
- Clustering - Unsupervised learning and data grouping
- Regression - Predictive modeling and regression analysis
- Deployment - Model deployment and production strategies
- Start with Fundamentals (Python, NumPy)
- Learn Data Processing (Pandas, basic visualization)
- Practice Visualization (Matplotlib)
- Begin with Machine Learning (Scikit-learn basics)
- Explore Scientific Computing (SciPy)
- Implement ML Algorithms from scratch (supervised learning)
- Learn Deep Learning Frameworks (PyTorch/TensorFlow)
- Build Computer Vision projects (OpenCV, basic architectures)
- Work with NLP tools (NLTK, spaCy)
- Master Deep Learning Architectures (CNNs, RNNs, Transformers)
- Explore Modern Computer Vision (YOLO, Vision Transformers, Diffusion Models)
- Work with Advanced NLP (BERT, GPT, T5, transformer fine-tuning)
- Build End-to-end Projects with deployment strategies
- Specialize in specific domains (Computer Vision, NLP, or MLOps)
Each guide follows a consistent, comprehensive structure:
- Installation instructions with dependencies
- Core concepts and fundamental usage
- Practical examples with real-world applications
- Advanced features and optimization techniques
- Best practices and common patterns
- Integration with other tools
- Algorithm overview and mathematical foundations
- When to use it - problem types and data characteristics
- Strengths & weaknesses analysis
- Important hyperparameters and tuning strategies
- Key assumptions and requirements
- Complete implementation with step-by-step explanations
- Evaluation methods and performance metrics
Navigate to any directory to find comprehensive quick reference guides. Each readme.md file contains practical examples and explanations for the respective technology.
- 54 comprehensive guides covering essential AI/ML technologies
- 8 major categories from fundamentals to advanced projects
- Complete code examples with explanations for every technology
- Production-ready implementations with best practices
- Consistent documentation format for easy navigation
Feel free to contribute by:
- Adding new quick references
- Improving existing documentation
- Fixing errors or typos
- Suggesting new technologies to include
This project is for educational purposes. Please respect the licenses of individual libraries and frameworks referenced.
Happy Learning! 🎉
Building AI knowledge one algorithm at a time.