What is the difference between machine learning and deep learning?

Deep learning is a subset of machine learning. The main difference is that deep learning uses multi-layer neural networks to learn representations from data, while machine learning is a broader field that also includes algorithms such as linear regression, decision trees, random forests, support vector machines, and gradient-boosted trees.

Artificial Intelligence
        ↓
Machine Learning
        ↓
Deep Learning

Machine learning

Machine learning develops models that learn patterns from data and use those patterns to make predictions or decisions.

A traditional supervised machine-learning workflow may look like:

Raw data
   ↓
Data preparation
   ↓
Feature engineering / feature selection
   ↓
ML algorithm
   ↓
Prediction

For example, when predicting house prices, useful inputs might include floor area, number of bedrooms, location-derived variables, and property age. These features can be supplied to models such as linear regression or gradient-boosted trees.

Deep learning

Deep learning uses neural networks containing multiple computational layers.

Input
  ↓
Layer 1
  ↓
Layer 2
  ↓
Layer 3
  ↓
...
  ↓
Output

These networks can learn useful internal representations as part of training. This is particularly valuable for high-dimensional unstructured data such as images, audio, and text.

For example, an image model can learn representations useful for recognizing objects without a developer manually specifying every visual feature.

Key differences

• Scope: Machine learning is the broader field; deep learning is a subfield of machine learning.
• Models: Machine learning includes regression, trees, SVMs, boosting, neural networks, and more. Deep learning primarily uses multi-layer neural networks.
• Feature engineering: Traditional ML often relies more on manually designed or selected features, while deep learning can learn many useful representations directly from data.
• Data: Traditional ML can work very well on smaller or structured datasets. Deep learning often benefits from larger datasets.
• Compute: Many ML models train efficiently on CPUs, while deep-learning training often benefits from GPUs or other accelerators.
• Training time: Traditional ML is often faster to train; deep learning can be significantly more computationally expensive.
• Unstructured data: Deep learning is especially effective for images, audio, text, and other high-dimensional unstructured data.
• Interpretability: Some traditional ML models are easier to inspect, while deep networks are often harder to interpret directly.
• Scaling: Many deep-learning architectures continue to benefit from more data, parameters, and compute.

Example: image classification

A traditional ML pipeline might involve explicitly extracting useful image features before passing them to a classifier:

Image
  ↓
Feature extraction
  ↓
Feature vector
  ↓
Classifier
  ↓
Prediction

A deep-learning approach can train a neural network to learn task-relevant representations and the final prediction function together:

Image
  ↓
Deep neural network
  ↓
Learned representations
  ↓
Prediction

This does not mean deep learning requires no preprocessing or domain knowledge. Data quality, architecture, objective design, evaluation, and training strategy remain important.

When traditional machine learning may be preferable

Deep learning is not automatically the best choice.

Traditional ML models can be strong choices when:

• The dataset is relatively small.
• The data is primarily structured or tabular.
• Training speed or computational cost is important.
• Model behavior needs to be easier to inspect.
• A simpler model already provides sufficient performance.

Tree-based methods, for example, are often strong baselines for tabular prediction problems.

When deep learning is especially useful

Deep learning is commonly used when working with:

• Images and video
• Speech and audio
• Natural language
• Large-scale representation learning
• Generative modeling
• Complex high-dimensional signals

Modern systems may also combine both approaches. A deep model can generate embeddings, for example, and those embeddings can then become features for another machine-learning model.

In short, machine learning is the broader discipline of learning predictive patterns from data, while deep learning is a machine-learning approach centered on multi-layer neural networks and learned representations.