Deep learning is a subset of machine learning: machine learning covers all methods that learn patterns from data, while deep learning specifically uses neural networks with many layers that learn features automatically from raw data such as images, audio and text. Classic machine learning works best on structured, tabular data with less computing power, and deep learning shines on large, unstructured data when you have GPUs and lots of examples. This guide explains the difference with examples, a comparison table and advice on which to learn first.
Key takeaways
- Artificial intelligence contains machine learning, and machine learning contains deep learning; they are nested, not competing, ideas.
- In classic machine learning a person often designs the input features; deep learning networks learn useful features from raw data themselves.
- For tabular business data such as sales, churn or credit risk, gradient boosting and other classic models often match or beat deep learning.
- Deep learning needs more data and computing power, usually GPUs, and its models are harder to explain.
- Learn Python, statistics and classic machine learning with scikit-learn first, then move to deep learning with PyTorch or TensorFlow.
How AI, machine learning and deep learning relate
- Artificial intelligence (AI): the broad goal of making computers perform tasks that need human-like intelligence. It includes rule-based systems that do not learn at all.
- Machine learning (ML): a part of AI where systems learn patterns from data instead of following hand-written rules.
- Deep learning (DL): a part of ML that uses multi-layer neural networks. Modern image recognition, speech recognition and large language models are built on deep learning.
Deep learning vs machine learning: comparison table
| Point | Classic machine learning | Deep learning |
|---|---|---|
| Typical data | Structured tables (rows and columns) | Unstructured data: images, audio, text, video |
| Feature engineering | Mostly done by people | Learned automatically by the network |
| Amount of data | Works with hundreds to thousands of rows | Usually needs large datasets, or a pre-trained model |
| Hardware | A normal laptop CPU is often enough | GPUs or TPUs for training |
| Training time | Seconds to minutes for many problems | Hours to days for large models |
| Explainability | Easier, for example tree feature importance | Harder; needs special explanation tools |
| Example algorithms | Linear and logistic regression, decision trees, random forest, XGBoost, k-means | CNNs, RNNs and LSTMs, transformers |
| Main libraries | scikit-learn, XGBoost, LightGBM | PyTorch, TensorFlow, Keras |
How machine learning works
In classic machine learning you prepare features that describe each example, then an algorithm learns the relationship between features and the target. For a loan default model, features might be income, existing loans, repayment history and loan amount. Much of the skill is in cleaning data and designing good features.
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
model = RandomForestClassifier(random_state=42).fit(X_train, y_train)
print(model.score(X_test, y_test))This trains a random forest on a tabular dataset that ships with scikit-learn, in a few seconds on any laptop.
How deep learning works
A neural network is made of layers of simple units. Each layer transforms its input and passes it on. With many layers, early ones learn simple patterns and later ones combine them. In an image model, the first layers detect edges, middle layers detect shapes such as eyes or wheels, and the final layers recognise whole objects. Nobody hand-codes those features; the network learns them from labelled examples through a process called backpropagation.
import torch.nn as nn
model = nn.Sequential(
nn.Flatten(),
nn.Linear(28 * 28, 128),
nn.ReLU(),
nn.Linear(128, 10)
)This small PyTorch network could classify 28 by 28 pixel images of handwritten digits into 10 classes. Real image models use convolutional layers or transformers and many more parameters.
Real-world examples
| Problem | Usual approach |
|---|---|
| Predicting customer churn from account data | Machine learning (gradient boosting) |
| Fraud scoring on transactions | Machine learning, sometimes combined with deep learning |
| Sales forecasting for a retail chain | Machine learning and time-series models |
| Detecting defects in factory product photos | Deep learning (computer vision) |
| Speech to text and voice assistants | Deep learning |
| Chatbots and text generation | Deep learning (large language models) |
When to choose machine learning or deep learning
Choose classic machine learning when
- Your data is in tables with clear columns.
- You have limited data or limited computing power.
- Stakeholders need to understand why the model made a decision.
- You need a working baseline quickly.
Choose deep learning when
- Your data is images, audio, video or free text.
- You have a lot of data, or you can fine-tune a pre-trained model.
- Accuracy matters more than explainability, and you have GPU access.
Which should you learn first?
Machine learning. Deep learning builds on the same ideas: training and test splits, loss functions, overfitting, evaluation metrics and gradient descent. Learn Python, pandas, statistics and scikit-learn, build two or three tabular projects, then move to neural networks. Our data science roadmap lays out the order, and our data science projects for beginners give you portfolio ideas for both stages.
Frequently asked questions
Is deep learning better than machine learning?
Not in general. Deep learning is better for unstructured data at scale, while classic machine learning is often as accurate, faster and easier to explain for tabular business data. The right choice depends on the problem.
Is ChatGPT machine learning or deep learning?
Deep learning. Large language models are deep neural networks based on the transformer architecture, and deep learning is itself part of machine learning.
Do I need a GPU to learn deep learning?
Not to start. Small models train on a CPU, and free notebook services such as Google Colab or Kaggle offer limited GPU time. Check their current terms, as free limits change.
Is deep learning asked in data science interviews?
For general data scientist roles, interviews focus more on statistics and classic machine learning, with basic neural network questions. Roles in computer vision or NLP go much deeper. Practise with our data science interview questions.
Explore more in our data science section.
