Supervised, Unsupervised, and Reinforcement Learning are the three main types of machine learning. Here’s how they differ:

Supervised Learning

• Definition: The model is trained using labeled data, meaning each input comes with a corresponding correct output.
• Goal: The algorithm learns a mapping from inputs to outputs and makes predictions on new data.
• Example Algorithms: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Neural Networks.
• Use Cases:
  • Spam detection in emails (Spam or Not Spam)
  • Image classification (Dog or Cat)
  • Loan approval prediction

Unsupervised Learning

• Definition: The model is trained on unlabeled data, meaning there are no predefined outputs.
• Goal: The algorithm finds patterns, structures, or relationships in data without explicit guidance.
• Example Algorithms: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA).
• Use Cases:
  • Customer segmentation in marketing
  • Anomaly detection (fraud detection)
  • Topic modeling in Natural Language Processing

Reinforcement Learning (RL)

• Definition: The model learns by interacting with an environment and receiving rewards or penalties for actions taken.
• Goal: The algorithm aims to maximize the cumulative reward over time.
• Key Concepts: Agent (learner), Environment, Actions, Reward, Policy.
• Example Algorithms: Q-Learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO).
• Use Cases:
  • Game playing (e.g., AlphaGo, Chess AI)
  • Robotics (self-learning robots)
  • Self-driving cars

Comparison Table