In addition, there are other differences, which we will explain below
Posted: Tue Feb 11, 2025 7:11 am
If you run a business, you know that adopting new technologies can solve problems and make your company more competitive. That's why many companies have accelerated their digital transformation during the upheaval caused by the COVID-19 pandemic.
Emerging technologies like machine learning have enormous potential and can help future-proof your business. But you should be cautious when buying: if you know little about how machine learning is applied, you risk getting useless results and wasting money. The example below shows what we mean.
We spoke to Thomas Wood , a data science consultant at Fast Data Science, who helped us break the topic down into easy-to-understand terms. With his help, we'll explain the differences between two common machine learning methods: supervised and unsupervised learning, and which use cases are best for each method.
Are you new to machine learning? First, familiarize yourself with these basic concepts:
Machine learning (ML) is a branch of artificial intelligence (AI) that solves problems by extracting knowledge from data using algorithms and statistical models. Generally speaking, all machine learning models can be divided into supervised and unsupervised learning.
In machine learning, an algorithm is a procedure that is applied to data to create a machine learning model.
A machine learning model is the output of a machine learning algorithm applied to data. A model therefore represents what has been learned by a machine learning algorithm.
In one sentence, the answer could be summarized as follows: The main difference between supervised and unsupervised learning is that supervised learning uses labeled data to predict the outcome, whereas unsupervised learning does not.
How supervised machine learning works
Supervised learning uses labeled data to train a model. But what does that mean italy telegram data in theory? Let's look at a few examples.
In supervised learning, the model is given both inputs and matching outputs. Let's imagine we are training a model to identify and classify different types of fruits. We give several images of fruits as input, along with their shape, size and color, and their taste characteristics. Next, we give the product the name of each fruit as output.
After a while, the algorithm will recognize a pattern between the features of the fruits (the inputs) and their names (the outputs). Once this happens, we can give the model new inputs and it will predict the outputs. This type of supervised learning, called classification, is the most common.
How Unsupervised Machine Learning Works
In unsupervised learning, on the other hand, we teach the model to recognize patterns even from unlabeled data. So the model receives inputs but no outputs.
Let's look at our fruit example again: In unsupervised learning, the model receives the input dataset (the images of the fruits and their properties), but not the outputs (the names of the fruits).
Emerging technologies like machine learning have enormous potential and can help future-proof your business. But you should be cautious when buying: if you know little about how machine learning is applied, you risk getting useless results and wasting money. The example below shows what we mean.
We spoke to Thomas Wood , a data science consultant at Fast Data Science, who helped us break the topic down into easy-to-understand terms. With his help, we'll explain the differences between two common machine learning methods: supervised and unsupervised learning, and which use cases are best for each method.
Are you new to machine learning? First, familiarize yourself with these basic concepts:
Machine learning (ML) is a branch of artificial intelligence (AI) that solves problems by extracting knowledge from data using algorithms and statistical models. Generally speaking, all machine learning models can be divided into supervised and unsupervised learning.
In machine learning, an algorithm is a procedure that is applied to data to create a machine learning model.
A machine learning model is the output of a machine learning algorithm applied to data. A model therefore represents what has been learned by a machine learning algorithm.
In one sentence, the answer could be summarized as follows: The main difference between supervised and unsupervised learning is that supervised learning uses labeled data to predict the outcome, whereas unsupervised learning does not.
How supervised machine learning works
Supervised learning uses labeled data to train a model. But what does that mean italy telegram data in theory? Let's look at a few examples.
In supervised learning, the model is given both inputs and matching outputs. Let's imagine we are training a model to identify and classify different types of fruits. We give several images of fruits as input, along with their shape, size and color, and their taste characteristics. Next, we give the product the name of each fruit as output.
After a while, the algorithm will recognize a pattern between the features of the fruits (the inputs) and their names (the outputs). Once this happens, we can give the model new inputs and it will predict the outputs. This type of supervised learning, called classification, is the most common.
How Unsupervised Machine Learning Works
In unsupervised learning, on the other hand, we teach the model to recognize patterns even from unlabeled data. So the model receives inputs but no outputs.
Let's look at our fruit example again: In unsupervised learning, the model receives the input dataset (the images of the fruits and their properties), but not the outputs (the names of the fruits).