Unsupervised learning: Difference between revisions
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'''Unsupervised learning''' is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between [[supervised learning]] and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than [[supervised learning]]. Its goal is to automatically find structure in a dataset, to find the regularities in the input, to see what normally happens. Unsupervised learning assumes there is a structure to the input space which implies there are certain patterns that occur more often than others. | '''Unsupervised learning''' is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between [[supervised learning]] and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than [[supervised learning]]. Its goal is to automatically find structure in a dataset, to find the regularities in the input, to see what normally happens. Unsupervised learning assumes there is a structure to the input space which implies there are certain patterns that occur more often than others. | ||
While in supervised learning, the computer learns by making use of labeled data, in unsupervised learning, the computer learns by making use of unlabeled data. | |||
Accuracy is not a measure analized with unsupervised learning. | Accuracy is not a measure analized with unsupervised learning. | ||
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== Resources == | == Resources == | ||
* [https://www.youtube.com/watch?v=yteYU_QpUxs Unsupervised Machine Learning Explained For Beginners. <code>youtube.com</code>] | |||
* [https://en.wikipedia.org/wiki/Unsupervised_learning Unsupervised learning. <code>wikipedia.org</code>] | * [https://en.wikipedia.org/wiki/Unsupervised_learning Unsupervised learning. <code>wikipedia.org</code>] | ||
* [https://www.ibm.com/cloud/learn/unsupervised-learning Unsupervised Learning. <code>ibm.com</code>] | * [https://www.ibm.com/cloud/learn/unsupervised-learning Unsupervised Learning. <code>ibm.com</code>] | ||
Latest revision as of 02:05, 12 May 2022
Unsupervised learning is a type of machine learning algorithm which does not supervise the model, but lets the model work on its own. The biggest difference between supervised learning and unsupervised learning is that the former deals with labeled data, while the latter deals with unlabeled data. While supervised learning is about function approximation, unsupervised learning is about description. Unsupervised learning has more difficult algorithms than supervised learning. Its goal is to automatically find structure in a dataset, to find the regularities in the input, to see what normally happens. Unsupervised learning assumes there is a structure to the input space which implies there are certain patterns that occur more often than others.
While in supervised learning, the computer learns by making use of labeled data, in unsupervised learning, the computer learns by making use of unlabeled data.
Accuracy is not a measure analized with unsupervised learning.
Unsupervised learning is more representative of most real world problems.
Applications
The main applications of unsupervised learning are listed as follows.[1]
- Clustering: The process of grouping the given data into different clusters or groups. Some examples of clustering include:
- Organizing computer clusters.
- Social network analysis.
- Market segmentation.
- Astronomical data analysis.
- Visualization: The process of creating diagrams, images, graphs, charts, etc., to communicate some information.
- Dimensionality Reduction: The process of reducing the number of random variables under consideration by getting a set of principal variables.
- Finding Association Rules: The process of finding associations between different parameters in the available data.
- Anomaly Detection: The identification of rare items, events or observations which brings suspicions by differing significantly from the normal data.
Unsupervised learning techniques
See also
Resources
- Unsupervised Machine Learning Explained For Beginners.
youtube.com - Unsupervised learning.
wikipedia.org - Unsupervised Learning.
ibm.com - Lecture 1.3 — Unsupervised Learning — Machine Learning – Andrew Ng.
youtube.com - Unsupervised Machine Learning: Algorithms, Types with Example.
guru99.com - Unsupervised Learning explained.
youtube.com - Unsupervised Machine Learning: Crash Course Statistics.
youtube.com - Unsupervised Learning: Crash Course AI.
youtube.com - Unsupervised Learning | Unsupervised Learning Algorithms | Machine Learning Tutorial | Simplilearn.
youtube.com - Supervised vs Unsupervised vs Reinforcement Learning.
youtube.com - Unsupervised Learning explained.
youtube.com - Machine Learning Explained | Unsupervised Learning.
youtube.com - Unsupervised Learning | Clustering and Association Algorithms in Machine Learning.
youtube.com - Deep Unsupervised Learning.
youtube.com - Unsupervised Machine Learning - Hierarchical Clustering with Mean Shift Scikit-learn and Python.
youtube.com