The Difference Between Supervised and Unsupervised Learning.
In this blog, I am going to briefly glance over the basic definition of supervised and unsupervised learning algorithms. I hope this blog could be helpful for those who have just started to learn machine learning.
What is Machine Learning(ML)?
Before we dive into the section of supervised and unsupervised learning, let’s talk about machine learning. Machine learning can be described as a subset of artificial intelligence and, the power of machine learning is defined by extracting (pulling out) knowledge from data. It means that when you allow a machine to analyze your data and train itself based on that data, once your trained model is deployed, you can make predictions. Machine learning is a combination of three important sciences; computer science(CS), artificial intelligence(AI), and statistics. It is also known as predictive analytics. Even though both are characterized by data processing to predict the future outcome based on historical data, they are many differences between predictive analytics and machine learning. It is hard to name an application or a website that is not using machine learning models in its systems these days.
The use of machine learning is very broad in recent years. Here are some prime examples: Image processing, medical diagnosis, fraud detection, predictions, search engine result refining, and product recommendations.
Three Main Types of Machine Learning Algorithms
We can easily break apart machine learning into three different types — supervised learning, unsupervised learning, and reinforcement learning. In this blog, I will mainly focus on supervised and unsupervised learning. Let’s briefly scrutinize the most successful kinds of machine learning algorithms.
Supervised Learning
What is Supervised Learning? And why it became one of the most accomplishing learning methods in machine learning? Supervised learning is an approach in machine learning that automates the decision-making process by giving input data (labeled data) to find a path to produce the desired output given an input. In a nutshell, a machine learning algorithm that can learn from user input and output data is called Supervised Learning. For instance, determine whether a tumor is cancerous or benign based on medical data/image? The labeled data or input data is the image, and the output is either a cancerous or benign tumor.
The two types of supervised learning algorithms are Classification and Regression.
Unsupervised Learning
Unsupervised learning is another type of learning method. It is mostly used in exploratory analysis. It is just input data and, there is no defined output data. It means that we are providing input data, and the system has to discover a structure on its own. Algorithms for which the potential outcomes are unlabeled and inferences are made directly from data without feedback from known outcomes called Unsupervised Learning. For example, you’re in a flower shop, and you see a flower that you cannot identify the strain and the name. In this case, after exploring and comparing flowers next to each other and using them as a reference, you can identify them by different features such as color, leaves, stem, and petals. This method is called Clustering. This is how the unsupervised method works.
The two types of unsupervised learning algorithms are Dimensionality reduction and Clustering.
