AI Algorithms - When to use

Here's a clear, practical table of the most used AI/ML algorithms, categorized by type, along with their best applications:

Algorithm	Category	Appropriate Uses (Best When...)	Real-World Examples
Linear Regression	Supervised (Regression)	Predicting continuous numerical values with linear relationships	House price prediction, sales forecasting, demand estimation
Logistic Regression	Supervised (Classification)	Binary or multi-class classification with probabilistic outputs	Spam detection, churn prediction, disease risk assessment
Decision Trees & Random Forest	Supervised (Classification & Regression)	Interpretable models, handling mixed data types, and non-linear relationships	Credit scoring, customer segmentation, and medical diagnosis
Support Vector Machine (SVM)	Supervised (Classification & Regression)	High-dimensional data, clear margin separation	Image classification, text categorization, bioinformatics
K-Nearest Neighbors (KNN)	Supervised (Classification & Regression)	Small-to-medium datasets, non-parametric, instance-based learning	Recommendation systems, anomaly detection
Naive Bayes	Supervised (Classification)	Text data, fast probabilistic classification	Email spam filtering, sentiment analysis, document classification
K-Means Clustering	Unsupervised (Clustering)	Grouping similar data points when the number of clusters is known	Customer segmentation, image compression, market analysis
Principal Component Analysis (PCA)	Unsupervised (Dimensionality Reduction)	Reducing features while preserving variance	Feature extraction, visualization, noise reduction
Convolutional Neural Networks (CNN)	Deep Learning (Supervised)	Image, video, and spatial data	Facial recognition, medical imaging, and self-driving cars
Recurrent Neural Networks (RNN/LSTM)	Deep Learning (Sequence)	Sequential or time-series data	Stock price prediction, language translation, speech recognition
Transformer Models (e.g., BERT, GPT)	Deep Learning (Sequence & Generative)	Natural language processing, large-scale text & multimodal tasks	Chatbots, machine translation, content generation, summarization
Generative Adversarial Networks (GANs)	Generative (Unsupervised)	Generating realistic synthetic data	Image generation, deepfakes, drug discovery, data augmentation
Reinforcement Learning (Q-Learning, PPO)	Reinforcement Learning	Decision-making in dynamic environments with rewards	Game AI (AlphaGo), robotics, autonomous vehicles, resource optimization
Gradient Boosting (XGBoost, LightGBM)	Ensemble (Supervised)	High-performance tabular data competitions	Fraud detection, ranking systems, predictive maintenance
Autoencoders	Unsupervised / Generative	Anomaly detection, dimensionality reduction, and denoising	Fraud detection, image denoising, recommender systems

Quick Guidance for Choosing Algorithms:

• Tabular / Structured Data → Start with Random Forest, XGBoost, or Logistic Regression.

• Images / Vision → CNNs or modern Vision Transformers.

• Text / Language → Transformer-based models (BERT, GPT, etc.).

• Time Series → LSTM/GRU or Prophet + Transformers.

• Need Interpretability → Decision Trees or Logistic Regression.

• Need High Performance → Ensemble methods (XGBoost) or Deep Learning.

• No labeled data → Clustering (K-Means) or Autoencoders.