AI and Machine Learning MOC

Here’s an AI and Machine Learning Glossary formatted in Markdown, with 🔹 for beginner (need to know) and 🔸 for advanced (nice to know) terms.

This glossary aims to explain the concepts below in a simple, easy-to-understand way, even for someone new to the field. Each term includes a definition, an "Explain Like I'm Five" section, and links for further learning.

Core Concepts

• Artificial Intelligence 🔹
• Machine Learning 🔹
• Supervised Learning 🔹
• Unsupervised Learning 🔹
• Reinforcement Learning 🔸
• Deep Learning 🔹
• Neural Networks 🔹
• Algorithm 🔹

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) 🔹

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 🔸

Evaluation & Metrics

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

Ethics & Challenges

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

Tools & Frameworks

• TensorFlow 🔸
• PyTorch 🔸
• Scikit-learn 🔸

Advanced Topics

• Natural Language Processing (NLP) 🔹
• Computer Vision 🔹
• Transfer Learning 🔸
• Diffusion Models 🔸