📊 Classification of Machine Learning Algorithms
Machine Learning algorithms are grouped into three main categories based on how they learn from data.
🔹 Supervised Learning – Learns from labeled data for classification and regression tasks.
Examples: Linear & Logistic Regression, SVM, KNN, Decision Trees, Random Forest, Neural Networks.
🔹 Unsupervised Learning – Discovers hidden patterns in unlabeled data.
Examples: K-Means, Gaussian Mixture Models, Spectral Clustering, Hidden Markov Models, Autoencoders.
🔹 Reinforcement Learning – Learns through rewards and penalties to make optimal decisions.
Examples: Q-Learning, Policy Gradient, PPO, TRPO, DQN.
💡 Key Takeaway: Understanding these three learning paradigms is the foundation for building effective AI and Machine Learning solutions.
July 13, 2026 442 2
