What is machine learning?
Machine learning is a branch of AI where systems learn patterns from data instead of being programmed only with fixed rules. A model receives examples, detects statistical structure, and uses that structure to predict, classify, rank, or group new cases.
Main learning types
| Type | Goal | Example |
|---|---|---|
| Supervised learning | Learn from labeled examples. | Predict churn, classify invoices, detect spam. |
| Unsupervised learning | Find structure without labels. | Customer segmentation, anomaly grouping. |
| Reinforcement learning | Learn actions through rewards. | Robotics, optimization, game-like environments. |
Practical ML workflow
- Define the prediction or decision problem.
- Collect and clean data.
- Create useful features or representations.
- Train a baseline model.
- Evaluate with appropriate metrics.
- Deploy carefully and monitor performance.
- Retrain or improve when data changes.
Important metrics
Metrics depend on the problem. Classification uses accuracy, precision, recall, F1, ROC-AUC, and confusion matrices. Regression uses MAE, RMSE, and R². Business metrics can matter more than model metrics: time saved, false positives reduced, revenue protected, or manual review avoided.
Useful business cases
- Demand forecasting.
- Lead scoring.
- Document classification.
- Fraud or anomaly detection.
- Personalized recommendations.
- Maintenance prediction.
AI7Sky principle: a small well-measured baseline can be more valuable than a complex model that no one trusts.