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What is Machine Learning? An Introduction to Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence that enables systems to learn patterns from data and improve their performance on a task without being explicitly programmed for every rule.
How Machine Learning Works
Instead of writing hard-coded rules, an ML system is given examples (data) and learns the underlying pattern by adjusting an internal mathematical model until its predictions match the observed outcomes closely enough.
- Data: The examples the model learns from.
- Model: A mathematical function with adjustable parameters.
- Training: The process of adjusting parameters to minimize prediction error.
- Inference: Using the trained model to make predictions on new, unseen data.
Types of Machine Learning
| Type | Description | Example |
|---|---|---|
| Supervised Learning | Learns from labeled input-output pairs | Spam detection |
| Unsupervised Learning | Finds patterns in unlabeled data | Customer segmentation |
| Reinforcement Learning | Learns via trial, error, and reward | Game-playing agents |
Why Machine Learning Matters
ML powers many of the products people use daily — recommendation feeds, voice assistants, fraud detection systems, and search ranking — by automatically improving as more data becomes available, something traditional rule-based programs can't easily do.
Machine Learning vs Traditional Programming
- Traditional Programming: Rules + Data → Output.
- Machine Learning: Data + Output → Rules (the model learns the rules itself).
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