Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and make decisions or predictions without being programmed for specific tasks. Here's a quick breakdown:
1. Learning from Data
Instead of relying on explicit programming, machine learning algorithms analyze data, identify patterns, and make predictions or decisions based on that information.
2. Types of Machine Learning
Supervised Learning: The algorithm learns from labeled data (input-output pairs) to make predictions on new data.
Unsupervised Learning: The algorithm identifies patterns in data without predefined labels, often used for clustering or anomaly detection.
Reinforcement Learning: The algorithm learns by receiving feedback from its actions in an environment, improving over time.
3. Real-World Applications
Machine learning is used in applications like personalized recommendations (Netflix, Amazon), voice recognition (Siri, Alexa), and autonomous vehicles.
Conclusion
Machine learning powers many of the technologies we use daily, helping systems get smarter over time through data and feedback, making it a key part of modern AI advancements.
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