What is unsupervised machine learning

Machine learning is a method of teaching computers and machines to think and learn like human beings. It’s a subset of artificial intelligence, and it makes use of statistical models and algorithms to recognize patterns in data. Machine learning techniques are broadly categorized into two main types – supervised and unsupervised machine learning. Supervised machine learning involves training a machine learning model with labeled data, while unsupervised machine learning involves training a model with unlabeled data.

In unsupervised machine learning, there is no supervision or guidance provided to the machine learning model. It’s left to discover patterns and relationships in the data on its own. Unsupervised learning is used when the objective is to identify hidden patterns or groupings in the data that are difficult or impossible to identify manually.

Clustering is an example of an unsupervised machine learning technique. In clustering, the machine learning model groups data points into clusters based on their similarity. The algorithm used to perform clustering does not have any prior knowledge about what the clusters should look like or the number of clusters that exist. Rather, it discovers them based on the similarities in the data.

Another example of unsupervised machine learning is anomaly detection. Anomaly detection involves identifying unusual or abnormal data points that may indicate something unusual is happening. For example, in credit card fraud detection, anomaly detection can be used to spot transactions that differ from a user’s usual spending patterns.

One of the major benefits of unsupervised machine learning is that it can help identify patterns and features that may not have been considered otherwise. It is especially useful when the data presented is unstructured, and there is no obvious or predefined output or category. Unsupervised machine learning can help to uncover hidden patterns and relationships between variables, which can then be used to build more effective models.

In conclusion, unsupervised machine learning is a valuable technique for identifying hidden patterns and relationships within data. While it requires a bit more work in terms of data preparation and interpretation, it can provide valuable insights into data that may not have been evident with supervised techniques. Its ability to identify hidden patterns and features makes it a powerful tool in many industries, from finance to healthcare to e-commerce.