Key takeaways
- Deep learning is a specialized subset of machine learning, which is itself a subset of AI, so they’re nested, not rivals.
- The core split is feature work: classic machine learning needs humans to define features, while deep learning learns them automatically from raw data using neural networks.
- For most first data science jobs, solid machine learning with Python is enough, and deep learning matters most for images, audio, and language.
Table of Contents
How machine learning vs deep learning fit together
Machine learning and deep learning aren’t competitors, they’re nested inside one another. Artificial intelligence is the broad field, machine learning is a branch of it, and deep learning is a branch of machine learning. Every deep learning model is a machine learning model, but the reverse isn’t true.
That nesting is exactly why the two get mixed up. They overlap, they share vocabulary, and the names sound similar. The clearest way to keep them straight is to look at how each one actually learns from data, which is where they split.
What is machine learning?
Machine learning trains a model on data so it can make predictions without being handed explicit rules for every case. Classic algorithms like decision trees, linear regression, and support vector machines work well on structured data, the kind that lives in spreadsheets and databases.
The catch is that a human usually decides which features the model should pay attention to. If you’re predicting house prices, someone chooses that size, location, and number of rooms are the signals worth feeding in. That step is called feature engineering, and it’s a big part of the job.
You already meet machine learning every day. It’s the engine behind spam filters, product recommendations based on your purchase history, credit scoring, and the churn models that flag which customers are about to cancel.
What is deep learning?
Deep learning is machine learning built on neural networks with many layers, which is where the “deep” comes from. Instead of a person selecting features, the network learns them straight from raw data, with each layer capturing a higher-level pattern.
In an image, early layers might pick up edges, middle layers shapes, and later layers whole objects like faces or cars. That makes deep learning powerful for messy, unstructured data such as photos, audio, and text. The trade is that it needs far more data and much more computing power to get there.
If machine learning runs the quiet background systems, deep learning powers the headline ones: voice assistants, facial recognition, self-driving perception, and the large language models behind modern chatbots.
The key differences
Both approaches learn from data, but they diverge on what they need and what they’re good at. The table below lines up the differences that matter in practice.
| Aspect | Machine learning | Deep learning |
|---|---|---|
| Data needed | Smaller, structured datasets | Large volumes, often unstructured |
| Features | Defined by humans | Learned automatically from raw data |
| Hardware | Runs on a normal CPU | Usually needs GPUs |
| Training time | Seconds to hours | Hours to weeks |
| Interpretability | Easier to explain and debug | More of a black box |
| Best for | Tabular data, forecasting, classification | Images, audio, language, complex patterns |
The pattern behind the table is a trade-off. Deep learning buys accuracy on complex, unstructured problems, but it pays for that with more data, more compute, and less transparency. When a simpler model does the job well, most teams reach for it first.
When to use machine learning or deep learning
Pick classic machine learning when your data is structured, your dataset is modest, and you need to explain why the model decided what it did. That covers a huge share of real work in finance, marketing, and operations, where a clear, auditable answer beats a marginally more accurate one. A bank flagging likely loan defaults from tabular customer data is a textbook case.
Reach for deep learning when the data is unstructured and the patterns are too complex to hand-engineer: image recognition, speech, natural language, or recommendation at massive scale. Transcribing audio or reading medical scans are the kinds of jobs where letting the model find its own features genuinely pays off.
In practice, the two aren’t an either-or. Plenty of real systems use classic machine learning for one part of a pipeline and deep learning for another, chosen task by task.
Is deep learning part of machine learning?
Yes, deep learning is part of machine learning. It’s a specialized subset that uses multi-layered neural networks, so every deep learning model is a machine learning model, while plenty of machine learning, like a humble decision tree, is not deep learning.
The easiest mental picture is three circles nested inside each other. Artificial intelligence is the outer ring, machine learning sits inside it, and deep learning sits inside machine learning. You can read more about the wider field in this guide to what data science is.
Which should you learn first, machine learning or deep learning?
Learn machine learning first. Deep learning builds directly on machine learning ideas, so starting with neural networks usually means doubling back to fill in gaps you skipped.
A sensible order is Python first, then core machine learning with a library like scikit-learn, then an introduction to neural networks once the fundamentals are solid. By the time you get to deep learning, you’ll actually understand what the extra layers are doing and why.
Do you need deep learning for a data science job?
No, you usually don’t need deep learning for your first data science job. Most day-to-day data science runs on classic machine learning applied to structured business data, where speed and interpretability matter more than raw model power.
Deep learning becomes important in more specialized roles that work with images, audio, or language, and in dedicated AI engineering. For getting hired into data science, a strong grounding in machine learning with Python is what counts, which is what WBS CODING SCHOOL’s Data Science Course is built to deliver. If you want to go further into deep learning and AI, the longer one-year Data Science & AI program gives you the room to specialize.
Is deep learning harder to learn than machine learning?
Deep learning is generally harder to learn than machine learning, mostly because it comes later and leans more on math. Neural networks add concepts like layers, activation functions, and backpropagation on top of the machine learning basics, and they demand more data and compute to experiment with.
The good news is that you don’t face all of it at once. If you learn the fundamentals in order, deep learning arrives as a natural extension of what you already know, not a wall. Most of the difficulty comes from skipping the groundwork, not from the neural networks themselves.
From theory to real models
The fastest way to understand the difference is to build with both, not just read definitions. That’s how the WBS CODING SCHOOL Data Science Course approaches it.
Students start with classic machine learning in scikit-learn: a supervised-learning classification contest, and an unsupervised model that clusters songs into real Spotify playlists. From there they move into neural networks, so they see first-hand where deep learning earns its extra cost and where a simpler model quietly wins. It all runs on messy, real data and a live cloud stack rather than tidy textbook examples, and the whole program is fully fundable via Bildungsgutschein.
The goal isn’t to memorize which is which. It’s to know, in front of a real problem, which tool to reach for. That judgment is what employers actually pay for.
Related blogs
- Data analytics and data science: what is the difference?
- How to become a data scientist
- Can a non-techie learn AI?
Conclusion
Machine learning and deep learning aren’t rivals: deep learning is a powerful, data-hungry branch of machine learning, best saved for images, audio, and language. For most data careers, strong machine learning with Python is the foundation that matters, with deep learning as a later specialization. If you’d rather build that judgment on real projects than piece it together alone, WBS CODING SCHOOL’s Data Science Course teaches both from the ground up, and the longer one-year Data Science & AI program takes you deeper into deep learning. Both are fully fundable through Bildungsgutschein.
