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Machine Learning
A subset of AI where systems learn patterns from data and make predictions or decisions without being explicitly programmed.
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One of the core pillars of AI.
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Between traditional rule-based systems (Expert Systems) and modern Deep Learning/GenAI.
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Provides the foundation for many practical AI applications used in industry today.
Pros
- Automates decision-making at scale.
- Flexible: can be applied to structured and unstructured data.
- Improves with more data and better features.
Cons
- Requires labeled data (for supervised learning).
- Models can overfit or underfit if not designed carefully.
- Often seen as a “black box” with limited interpretability.
Use Cases
- Fraud detection in finance.
- Customer churn prediction in telecom/retail.
- Demand forecasting in supply chain.
- Email spam filtering.
- Customer segmentation for targeted marketing.
- Market basket analysis (“people who buy X also buy Y”).
- Anomaly detection in cybersecurity and IoT.