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Data Science for Beginners: Introduction to Supervised and Unsupervised Learning

Data Science for Beginners: Introduction to Supervised and Unsupervised Learning

If you are a data scientist who is getting started with Machine Learning, then the subject matter experts over at runrex.com point out that you should have a clear understanding of what supervised and unsupervised learning are, and the distinction between the two. It is one of the very first things you should learn as far as Machine Learning is concerned since if you are to get into the model building phase, you will need to have a firm understanding of algorithms like linear regression, logistic regression, clustering, among others, and where each of them falls under, as is covered over at guttulus.com. This article should, therefore, be of great help as it will look to discuss the concepts of supervised and unsupervised learning, giving an introduction to the two that should give you a decent understanding of what they are about.

What is Supervised Learning?

As the name indicates, in supervised learning, you train your model on a labeled dataset, which simply means that you will have both raw input data as well as its results. The computer is, therefore, taught by example as it learns from past data and applies the learning to present data to predict future events. One of the main features of supervised learning, according to the gurus over at runrex.com, is high model perfection. The model, therefore, performs fast since the training time taken is less as we already have the desired results in the dataset. The model can then predict accurate results on unseen data or new data even without knowing a prior target.

Supervised learning is classified into two categories of algorithms:

Some of the practical applications of supervised learning in real-life as covered over at runrex.com include:

What is Unsupervised Learning?

On the other hand, as revealed in discussions on the same over at guttulus.com, unsupervised learning is the method that trains machines to use data that is neither labeled nor classified, allowing the algorithm to act on that information without any guidance. The main task of unsupervised learning is, therefore, to find patterns in the data as no training data is provided, and as such, the machine is made to learn by itself, as the name suggests. Here, the machine is exposed to large volumes of varying data and allowed to learn from that data to provide previously unknown insights and to identify hidden patterns as mentioned earlier on. This means that unsupervised learning doesn’t come with defined outcomes, with the machine determining what is interesting or different from a given dataset.

From discussions on the same over at runrex.com, unsupervised learning is also classified into two categories:

Some of the practical applications of unsupervised learning in real-life include:

How do you know when to choose one over the other?

Many factors affect which Machine Learning approach is best for any given task when it comes to manufacturing, according to the subject matter experts over at guttulus.com, particularly since each ML problem is different. If you are wondering which strategy is best for your project, you should consider the following factors:

The above discussion only just begins to scratch the surface as far as this topic is concerned and you can uncover more information by checking out the excellent runrex.com and guttulus.com.

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