The two terms are often used interchangeably, but they are not the same thing. Knowing the difference tells you which method fits which problem.
What is machine learning?
Machine learning is when a computer learns from examples instead of having every rule written by hand. Forecasting next month’s demand from past sales, or predicting whether a customer will cancel a subscription, are typical cases.
Methods such as regression, decision trees and Random Forest belong to this family. They work well on tabular data with relatively little data, and their decisions are easier to explain.
What is deep learning?
Deep learning is the branch of machine learning that uses multi-layer artificial neural networks. It stands out on unstructured data such as images, audio and text. Recognizing an object in a photo or translating a text is done this way.
In return it needs more data, stronger hardware and longer training time.
Which one, when?
- Tabular data, limited data: Classical machine learning is usually enough and faster
- Images, audio, free text: Deep learning is clearly more successful
- When the decision must be explained: Simple models have the advantage
Where to start?
Jumping straight to neural networks is tempting, but the solid path is: first Python and data analysis, then classical machine learning, then deep learning. The concepts you need to understand why a model works well or badly are learned in the first two steps.