Two Types of Machine Learning: Supervised vs Unsupervised

Two Types of Machine Learning: Supervised vs Unsupervised

Introduction

You’ve probably heard about machine learning before. It’s the technology that allows computers to use data to make predictions and improve their performance. But what exactly is machine learning? How does it work? And how can you use it in your own products or services? This article will help answer those questions by introducing two types of machine-learning techniques: supervised and unsupervised.

Supervised Machine Learning

Supervised machine learning is used when you have a large amount of data that can be broken down into groups. The algorithm will use these groups to find patterns in the data, then it will predict future events based on past events. This type of machine learning is useful when you have a lot of historical information about your customers and want to use it to make decisions about their future behavior.

Unsupervised Machine Learning

Unsupervised machine learning is used to find patterns in data. It’s used for tasks like clustering, classification and regression. The goal of unsupervised learning is to find groups of similar objects (cluster analysis) or identify patterns that describe a set of observations (classification).

Unsupervised learning can also be used to create a model that describes the relationship between input variables and outputs. This type of modeling is known as regression analysis because it predicts continuous values rather than discrete categories like “spam” or “ham.”

Machine learning is used in many products and services today.

  • Machine learning is used in many products and services today.
  • Examples of machine learning products and services:
  • Amazon Echo, Google Translate, Apple Siri and Facebook’s ad targeting algorithms are all examples of ML applications.
  • How does it work? In the simplest terms possible, a computer program learns from data that has been labeled or categorized by people (for example, an image labeled “dog” or an audio recording labeled “meow”). It uses these labels to make predictions about new data without being explicitly programmed how to do so–in other words it figures out what works best on its own through trial and error over time (and sometimes lots of repetition).

Conclusion

I hope this article helped you understand the difference between supervised and unsupervised machine learning. If you want to learn more about other types of machine learning, check out our other articles on deep learning and artificial intelligence!

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