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💻Interactive•30 min

Supervised, Unsupervised & Reinforcement Learning

Master the three fundamental paradigms of machine learning with hands-on examples.

The Three Paradigms of Machine Learning

1. Supervised Learning

Uses labeled data to train models that can make predictions.

Classification Algorithms:

  • Logistic Regression
  • Decision Trees, Random Forest
  • Support Vector Machines (SVM)
  • Neural Networks

    • Regression Algorithms:

  • Linear Regression
  • Polynomial Regression
  • Gradient Boosting (XGBoost, LightGBM)

    • 2. Unsupervised Learning

    Finds hidden patterns in data without labeled examples.

    Clustering: K-Means, DBSCAN, Hierarchical Dimensionality Reduction: PCA, t-SNE, UMAP

    3. Reinforcement Learning (RL)

    Trains agents to make decisions by maximizing cumulative rewards.

    Key Algorithms:

  • Q-Learning
  • Deep Q-Networks (DQN)
  • Proximal Policy Optimization (PPO)
  • RLHF (Reinforcement Learning from Human Feedback)

    • 🎯 Key Takeaways

    • ✓Supervised: Learn from labeled data to make predictions
    • ✓Unsupervised: Find patterns in unlabeled data
    • ✓Reinforcement: Learn through trial, error, and rewards
    • ✓RLHF is used to align modern LLMs with human preferences

    📚 Additional Resources

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