Machine learning (ML) is a branch of AI in which systems learn patterns from data. Deep learning is a specialised subset of ML that uses neural networks with many processing layers.
What machine learning does
ML algorithms use examples to make predictions or decisions. Common methods include decision trees, random forests, linear models and support vector machines. Businesses use them for forecasting, fraud detection and customer analysis.
What deep learning does
Deep learning uses multi-layer neural networks to learn complex representations. It is widely used for image recognition, speech processing and natural language applications.
Key differences
| Factor | Machine learning | Deep learning |
|---|---|---|
| Scope | Broad set of methods | Neural-network subset |
| Data | Can work with smaller structured datasets | Often benefits from large datasets |
| Compute | Often less demanding | Can require more computing power |
| Features | May need manual feature design | Can learn features automatically |
Which should you choose?
Start with simpler ML when data is structured, explainability matters or resources are limited. Consider deep learning for complex image, audio or language tasks when sufficient data and compute are available.
FAQs
Is deep learning part of machine learning?
Yes, it is a subset.
Is deep learning always more accurate?
No. Results depend on data, design and evaluation.