In this article you will find simple explanations for machine learning in general, supervised and unsupervised learning.
1. What is Machine Learning
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, gradually improving its accuracy.
2. Supervised Vs Unsupervised learning
Supervised and unsupervised learning represent the two key methods in which machines (algorithms) can automatically learn and improve from experience.
what is supervised learning?
We have supervised learning when a computer uses given labels as examples to take and sort sets of data and thereby predict future events.
in supervised learning, people teach or train the machine using labeled data. Labeled data means that it is already labeled with the correct answer.
There are two types of supervised learning algorithms:
Régression : is a supervised machine learning technique that is used to predict continuous values.
Classification : machine learning classification algorithms are at the heart of a large number of data mining problems and tasks.
In supervised learning we can find this list of algorithms:
K Nearest neighbors
Linear SVC (Support Vector Classifier)
Support Vector Regression (SVR)
Regression trees (e.g. random forest)
Fisher's linear discriminant.
what is unsupervised learning?
Unsupervised learning sorts things out without using predefined labels. Unsupervised machine learning algorithms operate without human guidance.
There are two two categories of algorithms in Unsupervised learning :
Clustering : The goal of clustering is to separate groups with similar characteristics and then assign them into clusters.
Association : Association is about discovering interesting relationships between variables in large databases. we have association rules that aim to find associations between data objects in large databases.
the main unsupervised machine learning algorithms and techniques are:
K-stands for clustering
K-NN (k nearest neighbors)
Neural Networks / Deep Learning
Principal component analysis
Decomposition of singular values
Independent component analysis
Although we have highlighted the advantages and disadvantages of supervised and unsupervised learning, it is not very accurate to say that one of these methods has more advantages than the other.
Both methods of machine learning algorithms have a huge place in data mining and you need to know the difference between supervised and unsupervised learning.