They're not competing terms: one sits inside the other
Machine learning and deep learning are not two rival approaches you must choose between. Deep learning is one technique inside the broader field of machine learning. Every deep learning system is a machine learning system, but not the other way round. And machine learning in turn sits inside the even broader field of artificial intelligence.
What machine learning actually means
Machine learning is any system that improves at a task by studying examples instead of following rules written by hand. A classic example predicts house prices from size and location, or flags a card transaction as likely fraud from patterns in past transactions.
These systems are often mathematically simple, such as decision trees or linear regression, and many work well with modest amounts of data.
What makes deep learning different
Deep learning uses neural networks stacked in many layers (see Neural Networks Explained). Each layer picks up more complex patterns than the last. That makes it very good at messy, unstructured data such as images, audio and natural language, where the patterns are far too complex to write rules for.
Which one fits which job
- Simpler machine learning tends to win with limited data, with spreadsheet-style structured data (like sales history), or when you need to explain exactly why a decision was made.
- Deep learning tends to win with large amounts of data and problems involving images, audio, video or language.
A common surprise is that for ordinary tables of numbers, simpler methods often match or beat deep learning, and they are cheaper and easier to explain.
The trade-off worth knowing
Deep learning's power costs a lot: it usually needs far more data and computing power, and it is harder to explain why it produced a particular output. This is often called the "black box" problem. Simpler models are usually more transparent, which matters in areas like lending or healthcare, where someone may need to understand and challenge a decision.
Frequently asked
Is deep learning always better than regular machine learning?
No — for smaller, simpler, well-structured problems, simpler machine learning methods are often faster, cheaper, and just as accurate.
Do I need a lot of math to understand this?
Not to understand the concepts at this level. The math matters if you eventually want to build these systems yourself, covered later in the Technical track.
Are neural networks actually like a human brain?
Only loosely inspired by one. The structure borrows the idea of connected nodes, but the way they actually learn is mathematically very different from biological brains.