Machine learning and deep learning are both parts of artificial intelligence, but they work in different ways — like a smart student versus a super-specialised genius.
Machine learning (ML) is when a computer is trained to find patterns in data and make decisions or predictions. For example, a machine learning program can be trained to recognise spam emails by studying thousands of examples. Once trained, it can quickly label new messages as spam or not.
Deep learning (DL), on the other hand, is a special kind of machine learning that uses something called neural networks — a model inspired by the human brain. These networks have many layers (hence “deep”) and are great at handling complex tasks like recognising faces, translating languages, or even writing music.
Here’s a simple way to think about it:
If machine learning is like a smart calculator that needs human help to choose features (like colour or size) to make decisions, deep learning figures out those features on its own — even if the data is messy, like blurry images or background noise in audio.
All deep learning is machine learning, but not all machine learning is deep learning.