Supervised vs Unsupervised Learning Both of them are very popular machine learning algorithms currently in use. Supervised learning is all about labelled data where its algorithm does reverse engineering work as compared to traditional programs i.e. it predicts/maps the output to the input data.  Unsupervised is all about hidden patterns and structures from unlabeled data.

Supervised vs Unsupervised Learning

Unsupervised learning  – UML helps to find a hidden jewel in data by grouping similar things together. Data have no target attribute. The algorithm takes training examples as the set of attributes/features alone. In this post, I have summarised my whole upcoming book “Unsupervised Learning – The Unlabelled Data Treasure” on one page. This one-page guide is to know everything about unsupervised learning on a high level.

Supervised vs Unsupervised Learning

Unsupervised Learning; is one of three types of machine learning i.e. Supervised Machine Learning, Unsupervised Machine Learning (UML) and Reinforcement Learning. The most common method in UML is cluster analysis. Cluster analysis is used for exploring hidden patterns or grouping data behind data analysis. The algorithm used in this is to draw inferences from data sets consisting of input data without labels. In short, UML is

  • A technique with the idea to explore hidden gems/patterns.
  • To find some intrinsic structure in data.
  • That something can’t be seen with the naked eye requires a magnifier (UML)

Supervised Machine Learning – Machine learning algorithms “learn” from the observations. When exposed to more observations, the algorithm improves its predictive performance. SML through a historic data set can hunt for correct answers, and the task of the algorithm is to find them in the new data. Supervised Machine Learning is

  • Is a task of deducing function from labelled training data.
  • Making predictions based on evidence in the presence of uncertainty
  • Identifying patterns in given data with adaptive algorithms
Supervised vs Unsupervised Learning

As per Wiki – In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory signal).

Books + Other readings Referred

  • Research through Open Internet – NewsPortals, Economic development report papers and conferences.
  • Internet-based survey results from 30 AI experts
  • Personal experience of  @AILabPage members.

Points to Note:

All credits if any remains on the original contributor only. We have covered supervised and unsupervised machine learning where we make predictions from labelled historical data and find patterns from unlabeled data. In the past post, we have walked through unsupervised machine learning.

Feedback & Further Question

Do you have any questions about Machine Learning, Deep Learning of AI? Leave a comment or ask your question via email. Will try my best to answer it.

Machine Learning (ML) - Everything You Need To Know

Conclusion – Supervised Learning which is one of three types of machine learning. This post is limited to supervised learning to explorer its details i.e. what it is doing and can do for businesses as new electricity to power them up. This blog post I tried to performed a comparison of different supervised machine learning techniques in classifying FinTech data.

This blog post is an attempt to describes the best-known supervised techniques in relative detail but not to claim anything. The aim was to produce a lighter rephrase of supervised learning and review of the key ideas and not a simple list of all algorithms in this category.


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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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