Types of Machine Learning

Machine learning is broadly divided into three main types, based on how the model learns from data.

1. Supervised Learning

In supervised learning, the model is trained on labeled data, meaning the input comes with the correct output already attached. The algorithm learns the relationship between input and output, then uses that knowledge to predict outcomes on new data.

Example: Training a model with thousands of house prices and features like size and location, so it can predict the price of a new house.

2. Unsupervised Learning

In unsupervised learning, the model works with unlabeled data, meaning there is no predefined output. The algorithm tries to find hidden patterns or groupings within the data on its own.

Example: Grouping customers into segments based on their shopping behavior, without knowing in advance what those segments should be.

3. Reinforcement Learning

Reinforcement learning is based on a reward-and-penalty system. An agent interacts with an environment, takes actions, and learns by receiving rewards for good decisions and penalties for bad ones. Over time, it learns the best strategy to maximize rewards.

Example: A robot learning to walk by being rewarded each time it moves forward without falling.

Quick comparison

• Supervised learning uses labeled data to predict outcomes.
• Unsupervised learning uses unlabeled data to find patterns.
• Reinforcement learning uses rewards and penalties to learn actions.