Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn patterns from data and make predictions or decisions without being explicitly programmed. Instead of following a fixed set of rules, ML models improve their performance over time by analyzing data and adapting to new patterns.
Types of Machine Learning:
- Supervised Learning – The model learns from labeled data (input-output pairs).
- Examples: Spam detection, image recognition, fraud detection.
- Unsupervised Learning – The model finds patterns in data without labeled outputs.
- Examples: Customer segmentation, anomaly detection, recommendation systems.
- Reinforcement Learning – The model learns by interacting with an environment and receiving rewards or penalties.
- Examples: Robotics, game playing (e.g., AlphaGo), autonomous driving.
Applications of Machine Learning:
- Voice assistants (Siri, Alexa)
- Recommendation systems (Netflix, YouTube, Amazon)
- Healthcare (disease prediction, personalized medicine)
- Finance (fraud detection, stock market prediction)
- Autonomous vehicles
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