Machine learning is a branch of artificial intelligence in which a program learns to perform a task from data, instead of following rules written by hand by a developer.
Instead of coding if the message contains this word, it is spam, you give the model thousands of already-labeled examples and it works out the patterns on its own. There are three main families:
- supervised learning: learning from labeled examples (predicting a price, detecting fraud)
- unsupervised learning: finding groups or structures in raw data
- reinforcement learning: learning through trial and reward
Deep learning, which uses deep neural networks, is a subset of machine learning; it is what made LLMs possible. But many real-world problems (scoring, forecasting, recommendation) are still solved very well with more classic methods, which are simpler to explain and maintain.
Why it matters when hiring
Machine learning covers very different jobs: the Data Scientist explores and models, the ML Engineer puts models into production and maintains them, the Research Scientist advances the state of the art. The classic trap: hiring a research profile when the need is to industrialize. In the interview, ask which model the candidate put into production, how they measured its impact and what happened when its performance dropped.