AI Index

Welcome! This is a living, evolving glossary of key terms and concepts in Artificial Intelligence (AI) and Machine Learning (ML), designed to support accessible learning and deeper exploration.

Each entry includes:

• A clear, beginner-friendly definition

• An “Explain Like I’m Five” (ELI5) explanation

• Links for further learning and context (when available)

🔹 = Essential / beginner-friendly
🔸 = Advanced / nice-to-know

📚 How this Index is Organized

Rather than listing terms alphabetically, this glossary is organized by conceptual flow to support progressive learning. Terms are grouped into sections like Core Concepts, Training & Optimization, and Ethics & Challenges, and arranged to build understanding step-by-step from foundational ideas to more advanced or emerging topics.

You’re encouraged to explore based on your needs, interests, or learning goals.

🔄 This is a Living Document

This index is updated regularly as new tools, models, and ideas emerge in the rapidly changing world of AI. Check back often for new entries, refinements, and improved explanations.

Core Concepts

• Artificial Intelligence 🔹
• Machine Learning 🔹
• Supervised Learning 🔹
• Unsupervised Learning 🔹
• Reinforcement Learning 🔸
• Deep Learning 🔹
• Neural Networks 🔹
• Algorithm 🔹
• Autonomous Systems 🔸
• Generative AI (GenAI) 🔹
• Inference 🔸
• Model Fine-tuning 🔸

Common Algorithms & Models

• Linear Regression 🔹
• Regression 🔹
• Classification 🔹
• Decision Tree 🔹
• Random Forest 🔹
• Support Vector Machine (SVM) 🔸
• K-Means Clustering 🔹
• Clustering 🔹
• Generative Adversarial Network (GAN)
• Transformer Model 🔹
• Large Language Model (LLM) 🔹
• Convolutional Neural Network (CNN) 🔸
• Generative Pre-trained Transformer (GPT) 🔹

Training and Optimization

• Deterministic Quoting 🔸
• Training Data 🔹
• Test Data 🔹
• Feature Engineering 🔹
• Overfitting and Underfitting 🔹
• Bias and Variance Tradeoff 🔹
• Bias 🔹
• Loss Function 🔸
• Backpropagation 🔸
• Gradient Descent 🔸
• Hyperparameters 🔸
• Activation Function 🔸
• Chain of Thought Prompting🔸
• Feature Extraction 🔸
• Regularization 🔸
• Latent Variables 🔸
• Heuristics 🔸

Evaluation & Metrics

• Accuracy 🔹
• Precision & Recall 🔹
• Recall 🔹
• F1 Score 🔹
• Confusion Matrix 🔸
• ROC-AUC Curve 🔸

Ethics & Challenges

• Bias in AI 🔹
• Explainable AI (XAI) 🔸
• Fairness 🔹
• Interpretability🔹
• Hallucinations in AI 🔹
• Ethics in AI 🔹

Tools & Frameworks

• TensorFlow 🔸
• PyTorch 🔸
• Ethics in AI 🔹
• Scikit-learn 🔸

Advanced Topics

• Natural Language Processing (NLP) 🔹
• Multimodal Omnimodel Model 🔸
• Agentic AI 🔸
• Computer Vision 🔹
• Transfer Learning 🔸
• Diffusion Models 🔸
• AutoGPT / AI Agents 🔸
• Retrieval-Augmented Generation (RAG) 🔸
• Memory in AI Systems 🔸
• Embodied AI 🔸
• Open-ended Learning 🔸

Applications

• Chatbot 🔹
• Data Mining 🔸
• Chatbot 🔹
• Computer Vision 🔹