Supervised Learning Tutorial 📚

Supervised learning is a core machine learning paradigm where models learn to map inputs to outputs using labeled training data. Here's a structured guide to understanding its fundamentals:

🔍 Key Concepts

• Labeled Data: Input-output pairs used to train the model
• Feature Space: Variables that describe the input data
• Loss Function: Measures prediction error (🧠 Learn more)

🤖 Common Algorithms

• Linear Regression (📉 Visual Example)
• Decision Tree (🌳 Visual Example)
• Support Vector Machine (🤖 Visual Example)
• Neural Networks (🧠 Deep Dive)

💡 Real-World Applications

• Predicting house prices (📊 Case Study)
• Classifying emails as spam or not (📬 Implementation Guide)
• Medical diagnosis (🩺 Ethical Considerations)

🛠️ Practical Steps

1. Prepare your dataset (📂 Data Preprocessing)
2. Split into training and testing sets (🧪 Splitting Techniques)
3. Train the model (💻 Code Example)
4. Evaluate performance (📈 Metrics Overview)

For interactive exercises, check out our Hands-On Machine Learning Course 🚀