Understanding the Basics: Supervised vs Unsupervised Machine Learning
Machine learning is a field of study that allows computer systems to learn and improve from experience without being explicitly programmed. It is a subset of artificial intelligence that focuses on the development of algorithms that can teach themselves to learn from data. Two major types of machine learning methods are Supervised and Unsupervised learning.
Supervised Learning
Supervised learning is a technique in which a model is trained on labelled data. The model is provided with a set of inputs and corresponding outputs, or labels. It learns to map the input to the output and can then predict the output for new, unseen input data. Supervised learning algorithms are often used for prediction, classification, and regression problems.
Examples of Supervised Learning
One classic example of supervised learning is image classification, where the model is trained to recognize and classify objects based on labelled images. Another example is spam filtering, where the model is trained on labelled emails to label future emails as spam or not spam.
Unsupervised Learning
Unsupervised learning is a technique in which the model is not provided with labelled data. Instead, it is given a set of input data and must find patterns or relationships within the data, without external guidance. Unsupervised learning algorithms are often used for clustering, density estimation, and anomaly detection.
Examples of Unsupervised Learning
One common example of unsupervised learning is clustering, where the machine learning model groups similar objects together based on their characteristics. This technique is used in customer segmentation to group similar customers based on their purchasing behaviour. Another example is anomaly detection, where the model identifies unusual behaviour in data, such as fraud detection.
Conclusion
Both supervised and unsupervised learning have their own advantages and applications in machine learning tasks. The choice of which method to use depends on the type of problem being addressed and the available data resources. By understanding the differences between supervised and unsupervised learning, machine learning practitioners can choose the most suitable technique for their particular use case.
