Machine learning encompasses various techniques aimed at enabling systems to learn from data and improve over time. Below are key categories and methods:

📊 Supervised Learning

  • Definition: Uses labeled data to train models.
  • Common Algorithms: Linear Regression, Decision Trees, Support Vector Machines (SVM), Neural Networks.
  • Applications: Predictive analytics, classification tasks (e.g., spam detection).
Supervised_Learning

🧠 Unsupervised Learning

  • Definition: Finds patterns in unlabeled data.
  • Common Algorithms: K-Means Clustering, Principal Component Analysis (PCA), Autoencoders.
  • Applications: Customer segmentation, anomaly detection.
Unsupervised_Learning

🔄 Reinforcement Learning

  • Definition: Trains models through reward-based feedback.
  • Common Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradients.
  • Applications: Game playing (e.g., AlphaGo), robotics.
Reinforcement_Learning

🌐 Related Resources

For visual comparisons of techniques, check out our interactive guide. 📈