There are two types of Machine Learning:
- Supervised Learning
- Unsupervised Learning
- This is the most common type of machine learning algorithm.
- You get labelled data in this case. For example: In a group of shapes, you are told that there are squares and circles.
- The “right answers” are given. In the above point, like you were told that there are only two right answers you can get, a square or a circle in the group of shapes.
- In Supervised Learning, you can come across two kinds of problems:
- Regression Problem – For Continuous data
- Classification Problem – For Discrete data
- No labels are given in the data. For example: In a group of shapes, you will have to find out what are the possible shapes with no options given to start with.
- Clustering is most commmonly used in the case of unsupervised learning of data as it tries to find similar attributes in data and combines them together. Google News extensively uses clustering algorithm.
- Some other examples of Clustering can be:
- Cloud providers like Amazon Web Services, Microsoft Azure and Google Cloud Platform organize their huge computing resources at the data centers as clusters.
- Social Media companies like Facebook, Twitter, Instagram, etc. use your information and activity to analyse your social network and suggest you people and content accordingly.
- Service providers like Infosys, DHL, Accenture, etc. have customers from different industries such as automobiles, manufacturing, finance, etc. They use clustering algorithms to divide their customers into “Market Segments“.
- Audio processing softwares extensively use clustering algorithms to categorize audio sources and how to deal with them.
- NASA uses clustering to analyse the huge amounts of data they get from their different astronomical (pun intended) research projects.