The Two Types of Machine Learning

In practice, there are two main types of machine learning algorithms: supervised and unsupervised. The algorithms which rely on a training dataset is called a Supervised machine learning Algorithm. Based upon this dataset, it finds associations between certain inputs and their respective outputs.

In practice, there are two main types of machine learning algorithms: supervised and unsupervised.

• The algorithms which rely on a training dataset is called a Supervised machine learning Algorithm. Based upon this dataset, it finds associations between certain inputs and their respective outputs.
• Unsupervised machine learning algorithms focus on clustering certain values or objects based on their similarities.

Let’s get into the intuition of these algorithms with an example of Google’s “faces” feature to clear any confusion that may exist with the definitions stated above. As aforementioned, the “faces” feature in the Google photos app identifies various people that occur frequently in all of your pictures.

There are two different approaches that Google could’ve taken to implement this feature in its software.

First, it could’ve implemented a supervised machine learning algorithm. To do this, Google would need access to a training dataset that contained all the different people that would appear in your images along with a label or the prediction that the algorithm should be making — in this scenario, the name. An example is shown below:

unsupervised-learning ai supervised-learning recommendations machine-learning

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

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Supervised Learning vs Unsupervised Learning. This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.

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