Over time without being explicitly programmed. Instead of following predefined instructions, ML models identify patterns and relationships in data to make predictions or decisions.
Types of Machine Learning:
- Supervised Learning: The model learns from labeled data, making predictions based on past examples. Example: Spam detection in emails.
- Unsupervised Learning: The model identifies patterns in unlabeled data without prior knowledge. Example: Customer segmentation in marketing.
- Reinforcement Learning: The model learns through trial and error, optimizing actions to maximize rewards. Example: AI in gaming and robotics.
Applications of Machine Learning:
- Fraud Detection: Banks use ML algorithms to detect unusual transaction patterns and prevent financial fraud.
- Recommendation Systems: Platforms like Netflix and Amazon analyze user behavior to suggest personalized content.
- Healthcare Diagnosis: AI-powered tools assist doctors in diagnosing diseases from medical images and patient data.
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