Deciding between machine learning vs deep learning for your project? Deep learning is a subset of ML that uses layered neural networks and suits text and images; classic machine learning handles structured, tabular data well.
For the wider picture, see this overview of machine learning basics.
| Item | Value |
|---|---|
| Where machine learning sits | A subset of AI |
| Where deep learning sits | A subset of ML that uses artificial neural networks |
| Typical data for machine learning | Structured data, such as a database |
| Typical data for deep learning | Unstructured data, such as text and images |
| Training effort for ML models | Generally a significant amount of human effort |
These definitions come from Dataquest and TechTarget. Terminology and practice can change, so check the sources before you rely on them.
What machine learning does
Machine learning is a subset of AI that lets a computer system make predictions or decisions without being explicitly programmed to do so. You give a model data, it learns patterns from that data, and it then applies those patterns to new cases.
A familiar business example is an online retailer that reviews a customer’s purchases and preferences, then makes future purchase recommendations based on prior behaviors and choices. Another is a football dataset: tabular rows of matches and team statistics can train a model to predict whether a team wins, loses or draws.
Machine learning usually comes in these forms:
- Supervised learning: the model learns from labeled data, for example to predict a win or a loss.
- Unsupervised learning: the model learns from unlabeled data, for example to cluster bank customers into segments that get different marketing strategies.
- Reinforcement learning: the model learns by interacting with its environment and receiving a reward, for example to train a robotic arm to pick and place objects.
Data quality matters as much as quantity. Preparing the inputs is called feature engineering: converting text columns to numbers, dropping columns that add nothing, or collecting new ones. It is a common and important part of any machine learning workflow that can help improve a model’s performance.
What deep learning adds
Deep learning is a subset of ML that uses artificial neural networks to solve more complex problems that other machine learning algorithms might be ill-equipped for. Data passes through several hidden layers, and each layer contains nodes (neurons) that try to extract relevant features from the input.
The “deep” in the name corresponds to the number of hidden layers a model can have. If the layered structure is new to you, read what is a neural network first, then come back to the comparison.
Different network designs suit different jobs. Two common ones are:
- Convolutional neural networks: multiple layers that are mostly used for image processing and object detection.
- Recurrent neural networks: networks that use sequential data or time series data.
In practice, deep learning models can transcribe and summarize podcasts, lecture notes and meeting recordings. A lack of sufficient compute power long created barriers to neural network learning, and that constraint is part of why deep learning is a more recent arrival in everyday tools.
Machine learning vs deep learning: the practical differences
The clearest split is the type of data each approach handles. Machine learning typically depends on structured data in known, well-standardized formats, such as a database. Deep learning models ingest unstructured data such as text and images.
The second split is how much a person has to do. ML models generally require a significant amount of human effort to train, including collecting, cleaning and analyzing the data. Deep learning is described as needing even less human intervention, and deep networks can learn complex relationships without the need for explicit feature engineering.
The third split is data volume. Deep learning can require more data than traditional machine learning, because deep architectures have many more parameters and need more data to avoid overfitting. If that risk is unfamiliar, see what is overfitting in machine learning for a plain-language explanation.
Put simply, the trade-off looks like this:
- Machine learning: structured data, more manual feature work, a smaller model to manage.
- Deep learning: unstructured data, automatic feature extraction, and vast datasets required to build the models.
Which approach fits your business problem?
Start with the shape of your data. If your records sit in tables, such as customer histories, match results or transactions, a classic model like logistic regression or a decision tree is a reasonable place to begin. Those two are the algorithms Dataquest suggests for predicting which football team is likely to win a match.
If your inputs are free text, images or recordings, deep learning is the approach built for that kind of unstructured data. Check first that you have enough examples, since deep models are data-hungry and prone to overfitting when data is thin.
A short checklist helps before you commit:
- Name the prediction or decision you want, and whether you have labeled examples.
- Look at your data: structured tables point toward machine learning, unstructured text or images toward deep learning.
- Estimate how much data you can realistically collect and clean.
- Decide how much expert time you can spend on feature work and training.
Two realistic scenarios show how the checklist plays out. A retailer with purchase histories in a database can use machine learning to turn prior behaviors and choices into recommendations; the training work then centers on collecting, cleaning and analyzing that customer data. A team that wants to transcribe and summarize meeting recordings is working with unstructured audio, which is the territory where deep learning is applied.
Signals that point toward classic machine learning include:
- Your data already lives in tables or a database with standard formats.
- Your team can spend time on feature engineering, such as converting text fields to numbers or dropping columns that add nothing.
- You want to segment customers or predict an outcome from labeled history, without building a very large model.
Signals that point toward deep learning include:
- Your inputs are text, images or recordings rather than neat columns.
- You would rather have the network discover complex features itself than prepare them by hand.
- You can supply the vast datasets that these models call for, and you have the compute power to train them.
The effort trade-off is worth weighing honestly. Machine learning asks for more human effort in preparing and training, while deep learning shifts that effort toward gathering data and compute. Neither route removes the need for good data, because quality matters as much as quantity.
Neither approach replaces the other. Deep learning is a subset of machine learning, so the choice is about fit, not about one being a newer or better label.
Frequently asked questions
Is deep learning the same as machine learning?
Show answer
No. Deep learning is a subset of machine learning that uses artificial neural networks, while machine learning is the broader field of systems that learn from data.
Are neural networks part of machine learning?
Show answer
Yes. Neural networks are a type of ML algorithm, and they are typically the foundation of the algorithms used in deep learning models.
Does deep learning need more data?
Show answer
Usually, yes. Deep learning can require more data than traditional machine learning, and vast datasets are needed to build these models.
Can machine learning work without deep learning?
Show answer
Yes. Many machine learning tasks, such as classifying a team’s match result from tabular data, can use algorithms like logistic regression or decision trees instead of deep neural networks.
