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smartphone device , or when you browse the internet or social media, you will often be presented with several advertisements. The advertisements that appear are also the result of ML processing which will provide advertisements according to your personality. In fact, there are still many examples of the application of machine learning that you often encounter. Then the question is, how can ML learn? ML can learn and analyze data based on data provided at the start of development and data when ML has been used. ML will work according to the techniques or methods used during development. What are the techniques? Let’s look at it together.

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Techniques Artificial intelligence There are several techniques possessed by machine learning , but broadly ML has two basic learning techniques, namely supervised and unsupervised . Supervised Learning Supervised learning techniques are techniques that you can apply to machine learning that can receive information that already ws data exists in the data by providing certain labels. It is hoped that this technique can provide targets for output by comparing past learning experiences. Suppose you have a number of films that you have labeled with a certain category. You also have films in the comedy category including the films 21 Jump Street and Jumanji. Apart from that, you also have other categories.

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for example the horror film category such as The Conjuring and It. When you buy a new film, you will identify the genre and content of the film. Once the film is identified, you will save the film in the appropriate category. Unsupervised Learning Unsupervised learning techniques are techniques that you can apply to machine learning which are used on data Singapore Lead that does not have information that can be applied directly. It is hoped that this technique can help find hidden structures or patterns in data that does not have labels. Slightly different from supervised learning , you don’t have any data to use as a reference beforehand. For example, you have never bought a film at all, but at some point, you buy a number of films and want to divide them into several categories so they are easy to find.