Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems

Machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data.

Common machine learning problems

1) Understanding Which Processes Need Automation.

2) Lack of Quality Data.
3) Inadequate Infrastructure.
4) Implementation.
5) Lack of Skilled Resources

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

                           “Machine learning will automate jobs that most people thought could only be done by people.”
                                         ~Dave Waters