Dummies Notes – Supervised vs Unsupervised Learning


Broadly speaking, Machine learning problems can be classified into three different types such as following:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

In this post, you will visually learn about supervised and unsupervised learning.

Supervised vs Unsupervised Learning

The following is self-explanatory picture representing what is supervised and unsupervised learning techniques and how are they different.

Supervised vs Unsupervised Machine Learning Problems

Figure 1. Supervised vs Unsupervised Machine Learning Problems

Pay attention to some of the following:

  • Supervised learning: In supervised learning problems, predictive models are created based on input set of records with output data (numbers or labels). Based on the outcome/response or dependent variable, supervised learning problems can be further divided into two different kinds:
    • Regression: When the outcome or response variable is a continuous variable (numeric or number), it can be called as regression problems.
    • Classification: When the outcome or response variable is a discrete variable (labels), it can be called as classification problems.
  • Unsupervised learning: In unsupervised learning, patterns or structures are found in data and labelled appropriately.

Supervised and Unsupervised Learning Algorithms

The following diagram represents information in relation to algorithms which can be used in case of supervised and unsupervised machine learning.

Supervised vs Unsupervised Machine Learning Algorithms

Figure 2. Supervised vs Unsupervised Machine Learning Algorithms

Pay attention to some of the following:

  • Supervised learning algorithms
    • Regression: Linear regression, Support vector regression (SVR), ensemble methods, decision trees, neural networks
    • Classification: Support vector machine (SVM), discriminant analysis, Naive Bayes, K-Nearest Neighbours (KNN)
  • Unsupervised learning algorithms
    • Clustering: K-means, K-medoids, Hierarchical, Gaussian mixture, neural networks, hidden markov model


In this post, you learned (visually) about what is supervised and unsupervised learning and how are they different. Did you find this article useful? Do you have any questions or suggestions about this article in relation to understanding what is supervised and unsupervised learning? Leave a comment and ask your questions and I shall do my best to address your queries.

Ajitesh Kumar

Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.
Ajitesh Kumar

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