What is Machine Learning?

Machine Learning is a subset of AI. ML deals with programming algorithms (ML Models) based on input data, and then the ML models makes predictions or decisions when new input data is fed to it with out the need of explicit instructions from developers.

Machine learning learns from data, using training algorithms to recognize patterns and make data-based decisions. According to Arthur Lee Samuel, 'Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.' (Arthur Lee Samuel was an American pioneer in the field of computer gaming and artificial intelligence, who popularized the term 'machine learning')

What are the different categories of Machine Learning?

Machine Learning can be categorized into Supervised learning, Unsupervised learning, and Reinforcement learning.

Supervised learning: In Supervised learning the ML algorithm takes in labeled data as input (Features) and predict correct outcomes (Labels).

Example of supervised learning:

Unsupervised learning: In Unsupervised learning the ML algorithm takes in unlabeled data as input and outputs patterns or insights based on similarity characteristics in the input data.

Examples of unsupervised learning:

Reinforcement learning: In Reinforcement learning the ML algorithm takes in unlabeled data initially similar to unsupervised learning, but has access to labeled data later to refine the outcomes.

Example of Reinforcement learning: