How AI Works: Data → Intelligence → Action
Raw data flows through neural networks where algorithms learn patterns and relationships to generate intelligent outputs and predictions.
🧠 Foundational AI Concepts
- Artificial Intelligence (AI) – Machines mimicking human intelligence.
- Machine Learning (ML) – Systems learning patterns from data without explicit programming.
- Deep Learning – ML subset using multi-layer neural networks to understand complex data.
🗃️ Types of Machine Learning
- Supervised Learning – Training models with labeled data.
- Unsupervised Learning – Finding patterns in unlabeled data.
- Semi-supervised Learning – Mixing labeled and unlabeled data for training.
- Reinforcement Learning (RL) – Systems learning optimal actions through rewards and feedback.
📊 Key Algorithms and Techniques
- Linear Regression & Logistic Regression – Simple predictive modeling techniques.
- Decision Trees & Random Forests – Models that use branching logic to make decisions.
- Support Vector Machines (SVM) – Classifiers based on finding a hyperplane to separate data classes.
- Gradient Boosting & XGBoost – Advanced ensemble methods for high-accuracy predictions.
🕸️ Neural Networks and Architectures
- Artificial Neural Networks (ANN) – Basic neural nets simulating neurons.
- Convolutional Neural Networks (CNN) – Specialized for image and video data.
- Recurrent Neural Networks (RNN) & LSTM – Handle sequential data like text and speech.
- Transformers – Powerful architecture for NLP tasks (GPT, BERT).
📖 Natural Language Processing (NLP)
- Tokenization – Breaking down text into units for processing.
- Sentiment Analysis – Interpreting emotional tone from text.
- Named Entity Recognition (NER) – Identifying entities in text.
- Text Summarization – Automatically condensing lengthy text.
- Language Modeling – Predicting next words or sentences (GPT series).
🖼️ Computer Vision (CV)
- Image Classification – Categorizing images into classes.
- Object Detection – Identifying and locating objects within images (YOLO).
- Segmentation (Semantic & Instance) – Pixel-level classification of image content.
- Facial Recognition – Identifying or verifying human faces.
⚡️ Generative AI
- Generative Adversarial Networks (GANs) – Generating realistic images, videos, or audio.
- Diffusion Models – Creating detailed and coherent images (Stable Diffusion).
- Variational Autoencoders (VAEs) – Generating new data by compressing and reconstructing input.
🎯 Reinforcement Learning (RL)
- Markov Decision Processes (MDP) – Framework for modeling decision-making.
- Q-Learning – RL algorithm for learning optimal policies.
- Deep Reinforcement Learning (DRL) – Combines RL with neural networks (Deep Q Networks, AlphaGo).
🔍 Explainable AI (XAI)
- Interpretability – Understanding model predictions clearly.
- Feature Importance – Determining influential data features.
- SHAP & LIME – Techniques for explaining individual predictions.
🔐 AI Ethics & Safety
- Bias & Fairness – Ensuring AI doesn’t reinforce harmful biases.
- Transparency & Accountability – Clear documentation and governance.
- Data Privacy & Security – Protecting user data in AI workflows.
🚀 Applied AI Concepts
- AI in Robotics – Integrating AI into physical automation.
- AI in Healthcare – Diagnosing diseases, treatment recommendations.
- AI in Finance (Fintech) – Fraud detection, predictive analytics.
- AI in Autonomous Vehicles – Enabling self-driving capabilities.