🤖 What is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables computers to learn from data and make decisions or predictions without being explicitly programmed for every scenario.

Instead of writing fixed rules, we provide examples (data), and the machine learns patterns to perform tasks like classification, prediction, or clustering.

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📌 Core Principles of ML

• Learning from Data: Models improve as they process more data.
• Generalization: The goal is to make accurate predictions on new, unseen data.
• Feedback Loop: ML systems evolve through retraining and performance evaluation.

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🆚 ML vs Traditional Programming

Traditional Programming	Machine Learning
Rules + Data → Output	Data + Output → Rules (Model)
Explicit instructions	Learns from patterns in data
Programmer defines logic	Algorithm finds logic
Less flexible	Adaptive and improves over time
Example: Calculator	Example: Spam Email Classifier

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💡 Real-World Examples

• 🛒 E-commerce: Product recommendations
• 📧 Email: Spam filtering
• 🏥 Healthcare: Disease prediction
• 🚗 Autonomous Vehicles: Object detection and decision-making

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📚 Learn More

• Supervised vs Unsupervised Learning
• Deep Learning (DL)
• AI vs ML vs DL Explained

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📌 Machine Learning bridges the gap between static programming and intelligent behavior—making it one of the most powerful tools in modern AI.