Machine learning is a branch of artificial intelligence that involves a computer and its calculations. In machine learning, the computer system is given raw data, and the computer makes calculations based on it. The difference between traditional systems of computers and machine learning is that with traditional systems, a developer has not incorporated high-level codes that would make distinctions between things. Therefore, it cannot make perfect or refined calculations. But in a machine learning model, it is a highly refined system incorporated with high-level data to make extreme calculations to the level that matches human intelligence, so it is capable of making extraordinary predictions. It can be divided broadly into two specific categories: supervised and unsupervised. There is also another category of artificial intelligence called semi-supervised.
With this type, a computer is taught what to do and how to do it with the help of examples. Here, a computer is given a large amount of labeled and structured data. One drawback of this system is that a computer demands a high amount of data to become an expert in a particular task. The data that serves as the input goes into the system through the various algorithms. Once the procedure of exposing the computer systems to this data and mastering a particular task is complete, you can give new data for a new and refined response. The different types of algorithms used in this kind of machine learning include logistic regression, K-nearest neighbors, polynomial regression, naive bayes, random forest, etc.