Basics of Machine Learning

Machine learning is a branch of artificial intelligence where systems learn patterns from data rather than being explicitly programmed with rules. Instead of writing every instruction, you give the model examples and let it infer the logic.

There are three broad types. Supervised learning trains on labeled examples to make predictions. Unsupervised learning finds structure in unlabeled data, such as clusters or associations. Reinforcement learning learns by trial and error through rewards and penalties.

A typical workflow involves collecting and cleaning data, choosing features, training a model, evaluating it on unseen data and then deploying it. The quality of your data usually matters far more than the choice of algorithm.

You do not need to boil the ocean to get value. Starting with a clear business question and a small, well-understood dataset is often the fastest path to a useful model.