Key Takeaways
- Machine learning trains models to make predictions based on data and selected features.
- Deep learning utilizes various neural networks with many layers to learn patterns directly from raw data.
- Machine learning works well with smaller, structured data and needs feature selection, while deep learning uses layered neural networks to handle large, unstructured data with more computing power.
If you’re not deeply involved in IT or data science, it’s completely normal to feel confused about how various fields actually differ. Many areas sound similar or overlap in certain ways under broader concepts, making it easy to mix them up, especially when their names sound similar.
Such is the case with deep learning and machine learning. Both are part of AI, and both involve systems learning from data to make decisions or predictions. Yet, despite these similarities, comparing deep learning vs. machine learning shows they’re actually quite different.
What Is Machine Learning
Machine learning (ML) is a subset of AI that is primarily focused on enabling computers to learn from data with minimal human intervention. In traditional programming, a human writes explicit rules for the computer to follow, but in machine learning, the computer learns the rules from examples.
For instance, instead of programming all the rules to recognize spam emails, you can feed a machine learning algorithm many example emails labeled as “spam” or “not spam,” and it will learn to distinguish them on its own.
Machine learning has become incredibly important because it powers so many technologies around us. Netflix and Amazon, for example, use machine learning to recommend movies and products viewers might like by learning from their past views and purchases.
Similar technology is also used in banking to detect fraudulent transactions, in smartphones to recognize your voice commands, in self-driving cars, and in healthcare to help doctors analyze scans for signs of diseases.
Types of machine learning models
Machine learning offers various ways for computers to learn from data. Each type of model has a distinct approach to how machines can understand information and make smart decisions.
The main types of ML models include:
Supervised learning
In supervised learning, the algorithm learns from labeled examples. So, humans provide input data and the correct output for each example. It’s like learning with an answer key. The model tries to generalize from the provided examples to predict the correct output for new, unseen inputs.
Unsupervised learning
In unsupervised learning, the algorithm works with data that doesn’t have labels or predefined answers. Instead of being told what each example is, it studies the data to uncover patterns and groupings on its own. This helps reveal hidden structures or relationships that might not be immediately visible.
Reinforcement learning
In reinforcement learning, the agent, like a computer program or system, interacts with its environment and learns from the outcomes of its actions. This agent receives rewards for good actions and penalties for bad ones, gradually figuring out which choices lead to the best results. It’s similar to learning through trial and error, as each decision influences what it does next.
What Is Deep Learning
Deep learning (DL) is a specialized subfield of machine learning. The term “deep” refers to the multiple layers within the neural network that deep learning algorithms use (inspired by the human brain’s network of neurons).
While a simple machine learning model might consider a few features of the data, a deep learning model has many layers of simulated “neurons,” allowing it to automatically learn increasingly abstract features from the raw data.
You can think of each layer as learning a higher-level representation: for example, in an image, early layers of a deep network might learn to detect edges, middle layers might detect shapes or textures, and final layers might recognize objects like faces or cars.
Types of deep learning models
Deep learning includes various types of models, each suited for specific kinds of data and tasks. Although they all use neural networks, their structures and purposes differ depending on what they are designed to handle.
Some of the most common types of deep learning models are:
Artificial neural networks (ANNs)
ANNs are the basic form of neural networks. They consist of an input layer, an output one, as well as one or more hidden layers, with each neuron in a layer connected to every neuron in the next. They are typically the foundation for more specialized deep learning models.
Convolutional neural networks (CNNs)
CNNs use special convolutional layers to detect patterns such as edges, textures, and shapes within images. This makes them highly effective for tasks like identifying objects in photos, facial recognition, analyzing satellite imagery, and interpreting medical scans to detect conditions or abnormalities.
Recurrent neural networks (RNNs)
RNNs are built to handle sequential data where order matters. Unlike other networks, RNNs have connections that loop back, enabling them to remember previous inputs as they process new ones. This makes them ideal for tasks where understanding the context of previous words or sounds is important for accurate results.
Generative adversarial networks (GANs)
GANs involve two networks working against each other: a generator that creates new data and a discriminator that checks if the data is real or fake. Through this competition, GANs become highly effective at generating realistic images, videos, or even music.
Key Differences Between Machine Learning and Deep Learning
It’s already clear that machine learning and deep learning are closely connected. In fact, deep learning is essentially a more advanced subset of machine learning. They both involve training models that make predictions or decisions based on data, with one using more complex neural networks that allow it to handle more detailed and unstructured data.
The differences between deep learning and machine learning become more apparent when we directly compare them in the following categories:
Data requirements
Machine learning models usually perform best when working with smaller amounts of data, such as a few hundred or thousand examples. They work especially well when that data is structured and clearly labeled.
In contrast to that, deep learning models require large datasets that often include thousands or millions of examples in order to reach their full potential. The reason behind this is that deep learning has many internal parameters to adjust, and without enough data, it risks memorizing training examples instead of learning general patterns.
Feature engineering
The way the two fields handle the details within data also differs. In most traditional machine learning approaches, people need to decide in advance what information the model should focus on. This is the process known as feature engineering. It applies to both supervised and unsupervised machine learning, as models rely on the features provided to find patterns or make predictions.
Deep learning works differently. Because of its many layers, it can analyze raw data directly and figure out for itself what matters most. This is why deep learning models do not need feature engineering; instead, they discover and learn such things on their own as they train.
Interpretability
Machine learning models, particularly simpler ones, are generally easier to interpret. That’s because you can usually see which features influenced a prediction and how they were weighted. So, they’re generally more transparent and explainable.
Deep learning models, however, are more challenging to comprehend, particularly in terms of how their layers and parameters interact to reach a decision. This lower interpretability can be a disadvantage in fields where explaining a prediction is as important as its accuracy.
Speed and resources
Machine learning models are typically faster to train and require less computational power. They can often be developed and deployed on standard computers without specialized hardware.
Deep learning models, on the other hand, need more time and resources to train due to their complexity and the size of the data they use. Training such models usually requires powerful GPUs (graphics processing units) or cloud computing services, and they consume more memory and energy compared to simpler machine learning models.
When to Use Machine Learning vs. Deep Learning
Understanding both approaches, particularly the differences between deep learning and machine learning, is necessary when deciding which to use for your project. Both have their strengths, and knowing when to apply each one naturally leads to better results.
While machine learning offers simplicity and clarity, deep learning unlocks powerful capabilities for more demanding tasks. Therefore, it’s preferred to use machine learning when:
- Your problem involves structured data with clear features.
- You need results quickly with smaller or limited datasets.
- Model explainability is important, such as in finance or healthcare.
- You’re building and testing early versions or proofs of concept for AI features.
On the other hand, deep learning might be more suitable when:
- You’re working with complex, unstructured data like images, audio, or natural language.
- You have large, high-quality datasets to support training.
- Maximum accuracy is needed, and interpretability is less critical to your use case.
- Your project is ready to scale up with advanced AI models to deliver stronger capabilities.
In practice, the best solution is one that balances performance with practicality. Sometimes, the simplest solution that meets the requirements is best, even if a fancier deep network could squeeze out a few extra percentage points. As you grow more comfortable, you’ll develop an intuition for which approach suits a given problem.
The best thing is that with the rise of user-friendly libraries (such as scikit-learn, TensorFlow, and PyTorch) and cloud services (through Microsoft Azure Machine Learning and IBM Watson), both ML and DL are becoming accessible even if you’re not an expert. So, you can experiment and see what works best for your situation.
A Decision That Shapes the Future
Just as it is important to choose whether to use machine learning or deep learning for a project, it is equally important to think about which path you might want to focus on in your career. If you’re in the middle of that road and not sure yet whether you want to make that choice, it can be helpful to begin with a broader foundation that gives you insights into both.
At Syracuse University’s iSchool, you can build that foundation through the Applied Data Science Bachelor’s Degree, add an Applied Data Science Minor to another program, or focus on a Master’s in Applied Human-Centered Artificial Intelligence, which explores both machine learning and deep learning as part of its curriculum.
AI has become crucial across many industries and roles. Both machine learning and deep learning are part of this shift, and it’s their combined power that keeps progress moving forward. So, whichever you choose, you’ll be contributing to a field that’s changing how the world works every day.
Frequently Asked Questions (FAQs)
When should I use machine learning instead of deep learning?
You should use machine learning when you have structured data, limited computing resources, or need quick, interpretable results.
Should I learn ML or DL?
You could start with machine learning in order to build strong fundamentals, and then explore deep learning so you can also work with more complex data tasks.
Do I need a lot of computing power to use deep learning?
Yes, deep learning models usually require powerful GPUs or cloud computing.
Can machine learning and deep learning be used together?
Yes, using ML for simpler tasks and DL for more complex parts of a project can help enhance the overall performance.