Unsupervised Machine Learning is an Artificial Intelligence algorithm that isn't under supervision during training datasets. It uses machine learning algorithms to analyze and cluster unlabeled data as it finds underlying data patterns without the aid of a human being. Its ability to discover similarities and differences in information makes it the ideal cross-selling strategy, image recognition, customer segmentation, etc. Unsupervised Machine learning cannot be directly applied to a regression or classification problem because it's unpredictable what the values of the output data would be making it impossible to train the model manually.
advantages of Unsupervised Machine learning
- Labeling data requires lots of time and expense, unsupervised learning solves the problem by learning the data without the aid of any labeled data.
- The labels can be added once the algorithm classifies them into different classes.
- This is a beneficial tool for a data scientist, as it can be of help to analyze raw data.
- In a way, unsupervised Machine learning is the same as human intelligence, as the algorithm learns through various errors and flaws over time by itself.
Disadvantages of Unsupervised Machine learning
- There's a high probability of Less accuracy of the results as we don't know what the resulting model would predict without any labeled data.
- It might take a long time during the training phase of the algorithm, as the algorithm analyses and calculate all possibilities.
- The algorithm is learning itself without the help of any prior labeled data.
- Machine learning has become popular as it is convenient for various corporates to check the bulk of the database at an instant, which usually takes up a long time doing manual observation.
Some of the real-world examples of unsupervised Machine Learning are,
Google News- Google news uses unsupervised learning algorithms to categorize various news.
Recommendation engines- using past purchase behavior data, many shopping websites uses this unsupervised learning algorithm to suggest And recommend new products to a buyer during the checkout process.