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Machine LearningvsDeep Learning
What is the difference between machine learning and deep learning?
Updated 2 min read7 differences
In short
Machine learning covers algorithms that learn from data; deep learning is a subset whose many-layered neural networks find features alone but need more data.
Machine Learning
Machine learning is a branch of artificial intelligence in which computers learn patterns from data to make predictions instead of following hand-written rules.
Read the page on Machine LearningDeep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from raw data such as images and text.
Read the page on Deep LearningMachine Learning and Deep Learning compared
| Aspect | Machine Learning | Deep Learning |
|---|---|---|
| Scope | The broad field of learning from data | A subset of ML based on deep neural networks |
| Features | Often designed by people (feature engineering) | Learned automatically from raw data |
| Data needed | Can work well with thousands of examples | Usually very large datasets or a pretrained model |
| Hardware | A regular CPU is often enough | Usually GPUs or other accelerators |
| Interpretability | Many models are fairly easy to explain | Hard to explain; often treated as a black box |
| Typical models | Linear regression, decision trees, gradient boosting | Convolutional networks, transformers, diffusion models |
| Best for | Tabular data, forecasting and risk scoring | Images, audio, text and generative AI |
The difference, explained
Machine learning (ML) is a branch of artificial intelligence in which programs learn rules from examples instead of being explicitly programmed; it includes methods like linear regression, decision trees and gradient boosting. Deep learning is a subset of machine learning that uses neural networks with many layers, which is where the word 'deep' comes from.
The practical difference is feature engineering. Classic ML usually relies on people to choose the input features, like a house's size, age and location, and then learns from them. Deep learning learns useful features directly from raw data such as pixels, audio or text, which is why it powers image recognition, speech and large language models, but it needs much more data and specialized hardware like GPUs.
They are layers of the same idea, not rivals: all deep learning is machine learning, but not all machine learning is deep. Many teams use both, for example a gradient-boosted model for fraud scoring on tabular data and a deep network for reading scanned documents or images.
A common misconception is that deep learning is always more accurate. On structured, tabular data, tree-based models such as gradient boosting still often match or beat neural networks while being faster to train and easier to explain. Deep learning shines when the data is unstructured and plentiful.
Which one should you use?
Choose Machine Learning when…
- Your data is structured, like rows in a spreadsheet or database.
- You have a modest dataset and a limited computing budget.
- You must explain predictions to users or regulators.
Choose Deep Learning when…
- Your data is unstructured, such as images, audio or free text.
- You have lots of data or can start from a pretrained model.
- Accuracy on complex patterns matters more than explainability.
Readers ask
Is deep learning part of machine learning?
Yes. Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence.
Are large language models deep learning?
Yes. LLMs are deep neural networks based on the transformer architecture, trained on huge amounts of text.
Should I learn machine learning before deep learning?
Usually, yes. Core ideas like training and test data, overfitting and evaluation metrics apply directly to deep learning and are easier to learn on simpler models.