Machine learning is a subset of AI focused on developing computer programs that can analyze data to make decisions or predictions. AI designers often use ML in their AI programs because it doesn’t have the limitations of rule-based techniques.

There are three common approaches to training ML programs:

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Supervised learning

In this approach, the ML program learns from a labeled training set. A labeled training set includes data that is labeled or tagged, which provides context and meaning to the data. For instance, an email spam filter that’s trained with supervised learning would use a training set of emails that are labeled as “spam” or “not spam.” Supervised learning is often used when there’s a specific output in mind.

Unsupervised learning

In this approach, the ML program learns from an unlabeled training set. An unlabeled training set includes data that does not have labels or tags. For instance, ML might be used to analyze a dataset of unsorted  email messages and find patterns in topics, keywords, or contacts. In other words, unsupervised learning is used to identify patterns in data without a specific output in mind.

Reinforcement learning

In this approach, the ML program uses trial-and-error to learn which actions lead to the best outcome. The program learns to do this by getting rewarded for making good choices that lead to the desired results. Reinforcement learning is commonly used by conversational AI tools. As these tools receive feedback from users and AI designers, they learn to generate effective responses.

Each ML technique has its own strengths and weaknesses. Depending on the type of data that’s available and what’s needed to solve the particular problem, AI designers may use one, two, or all three of these techniques to produce an AI-powered solution.