🤖 Welcome to the world of machine learning! This guide explores popular algorithms and their applications.

🔍 Key Categories of Machine Learning Algorithms

  1. Supervised Learning

    • Uses labeled data to train models
    • Common algorithms:
      • Linear Regression 📈
      • Decision Tree 🌳
      • Support Vector Machine (SVM) ⚖️
      • Neural Networks 🧠
    • Linear_Regression

  2. Unsupervised Learning

    • Works with unlabeled data
    • Common algorithms:
      • K-Means Clustering 🧩
      • Principal Component Analysis (PCA) 📊
      • Apriori Algorithm 🧾
    • K_Means_Clustering

  3. Reinforcement Learning

    • Learns through trial and error
    • Common algorithms:
      • Q-Learning 🔄
      • Deep Q-Network (DQN) 🤖
      • Proximal Policy Optimization (PPO) 📈
    • Reinforcement_Learning

📚 Expand Your Knowledge

Want to dive deeper into machine learning tutorials? Explore here to learn how to implement these algorithms in code!

🌐 Practical Applications

  • Image Recognition: Convolutional Neural Networks (CNNs)
  • Natural Language Processing: Recurrent Neural Networks (RNNs)
  • Recommendation Systems: Collaborative Filtering 🎯
  • Anomaly Detection: Isolation Forest 🕵️‍♂️
Neural_Networks

For more examples, check out our machine learning case studies section!