AI/ML models

AI/ML models vary from  basic reactive systems to complex deep learning, categorized broadly by learning style (Supervised, Unsupervised, Reinforcement) or capability (Narrow, General) , with specific algorithms like  Linear Regression ,  Decision Trees ,  SVMs ,  Neural Networks , and  Transformers  handling tasks from classification and prediction to image/language generation.   

 

 By Learning Style 

 

 Supervised Learning:  Learns from labeled data to make predictions (e.g., predicting house prices, classifying emails). 

 

 Examples:  Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, Naive Bayes. 

 

 

 Unsupervised Learning:  Finds patterns in unlabeled data (e.g., customer segmentation). 

 Reinforcement Learning (RL):  Learns through trial-and-error with rewards/penalties (e.g., game AI, robotics). 

 Semi-Supervised/Self-Supervised:  Uses a mix of labeled/unlabeled data or learns from data structure itself.   

 

 

 By Capability/Type 

 

 Narrow AI (Weak AI) :  Performs specific tasks (e.g., Siri, Google Search). 

 General AI (Strong AI) :  Human-level intelligence (theoretical). 

 Deep Learning Models :  Use neural networks with many layers for complex tasks like computer vision & NLP. 

 

 Examples:  Convolutional Neural Networks (CNNs) for images, Recurrent Neural Networks (RNNs) for sequences, Transformers for language (like GPT). 

 

 

 Rule-Based/Expert Systems:  Older AI using predefined rules.   

 

 

 Common Algorithms & Models 

 

 Regression:  Predicts continuous values (Linear, Logistic). 

 Classification:  Categorizes data (Naive Bayes, SVM, Decision Trees, KNN). 

 Clustering:  Groups similar data points (e.g., K-Means, used in Unsupervised Learning). 

 Ensemble Methods:  Combine multiple models (Random Forest, Gradient Boosting). 

 Transformers:  Revolutionized NLP and vision (e.g., GPT, BERT).   

 

 

 Modern Applications 

 

 Language Models (LLMs):  GPT, Claude, Gemini (Chat, Text Generation). 

 Image/Video Models:  Veo, Sora (Image/Video Generation). 

 Computer Vision Models:  Facial Recognition, Object Detection.