Machine Learning vs. Deep Learning: What's the Difference?
Read the comparison below, or explore it visually. Create a visual lesson comparing machine learning and deep learning using available credits or plan allowance.
Deep learning is a type of machine learning — the relationship is nested, not opposed. Machine learning is the broad field of algorithms that learn patterns from data instead of being explicitly programmed. Deep learning is a specialized subset that uses neural networks with many layers, which excel at messy, high-dimensional data like images, audio, and language. So all deep learning is machine learning, but plenty of machine learning (decision trees, linear regression) isn't deep learning.
At a glance
| Machine Learning | Deep Learning | |
|---|---|---|
| Relationship | The broad field | A subset of machine learning |
| How it works | Many algorithm types learn from data | Many-layered neural networks |
| Data needed | Works with smaller datasets | Usually needs large datasets |
| Features | Often hand-crafted by humans | Learned automatically by the network |
| Best at | Tabular data, simpler patterns | Images, audio, language |
Which should you use?
Use plain machine learning for structured/tabular data, smaller datasets, or when you need a fast, explainable model.
Deep learning shines on large, complex, unstructured data — photos, speech, text — where it can learn the features on its own.