{"id":615,"date":"2017-11-15T11:00:29","date_gmt":"2017-11-15T16:00:29","guid":{"rendered":"https:\/\/clickup.com\/blog\/?p=615"},"modified":"2021-05-03T20:30:54","modified_gmt":"2021-05-04T03:30:54","slug":"supervised-vs-unsupervised-machine-learning","status":"publish","type":"post","link":"https:\/\/clickup.com\/blog\/supervised-vs-unsupervised-machine-learning\/","title":{"rendered":"Supervised Vs. Unsupervised Machine Learning"},"content":{"rendered":"

Ever wondered how computers learn on their own? Yeah, me too! <\/span><\/p>\n

Ever since the intersection of lightning-fast hardware and brilliant software, machines have been learning how to think like humans. <\/span><\/p>\n

The otherworldly algorithms that drive growth and self-improvement actually exist! But how is any of this possible? The entirely rule-based system is called machine learning.<\/span><\/p>\n

It\u2019s not as complex as it sounds. At a high level, all machine learning algorithms can be classified into two categories, supervised and unsupervised learning. <\/span><\/p>\n

For the most part, you\u2019ll interact with the benefits of supervised learning at sites like Google, Spotify, Amazon, Netflix, and tons of new web apps, so let\u2019s start off with how supervised learning works.\u00a0<\/span><\/p>\n

Supervised learning<\/span><\/h2>\n

The important distinction here is supervised learning is guided by human intelligence, observation, and known outcomes. <\/span><\/p>\n

In other words, we tell an algorithm the difference between \u201cright\u201d and \u201cwrong\u201d and ask them to mimic those results when new information is thrown their way. <\/span><\/p>\n

With a well-built algorithm, the machine will be able to create a model to decipher patterns and improve its own efficiency, becoming more accurate over time. <\/span><\/p>\n

The biggest negative with this type of learning is having enough data that includes all anomalies and edge-cases to accurately teach the algorithm how to handle each unique situation.<\/span><\/p>\n

Supervised learning can be further divided based on the prediction method used.<\/span><\/p>\n