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Types of ML Models
Supervised Learning
Data has input features (X) and target labels (y).
Model learns mapping: f(X) → y.
Examples:
- Regression -> Predicting house prices, demand forecast, server usage.
- Classification -> Spam vs Non-spam email or Customer churn.
Unsupervised Learning
Data has inputs only, no labels.
Goal: find hidden patterns or structure.
Examples:
- Clustering -> Customer segmentation.
- Association Rules -> Market basket analysis (“people who buy X also buy Y”).
- Dimensionality Reduction -> Principal Component Analysis (PCA) for visualization.
- Taking a high dimensional data and reducing it to fewer dimensions.
Reinforcement Learning (RL)
Agent interacts with environment -> learns by trial and error.
Used for decision-making & control.
Examples:
- Robotics & self-driving cars.
- Newer Video Games.
- OTT Content recommendations.
- Ads.
Semi-Supervised Learning
Mix of few labeled + many unlabeled data points.
Often used in NLP and computer vision.
Example: labeling 1,000 medical images, then using 100,000 unlabeled ones to improve model.