Updated: May 5, 2020
The term ‘Machine Learning’ refers to the automated detection of meaningful patterns in data. Machine Learning (ML) is sometimes also called as ‘Automated Learning’.
In this article, I am going to talk about Data Science, Machine Learning, Statistics and Probability and many more detailed subjects which a Data Scientist use in their routine jobs.
When do we need Machine Learning?
Tasks where there can be a defect: Human perform many tasks routinely; however, they fail sometimes. Ex: Driving. In such cases if we program efficiently and provide enough training data the program will learn from its experience and achieve satisfactory results.
Task where Human cannot go or perform: Human is one of the intelligent species on earth, however there are few tasks where human has no capability to handle such huge or micro elements. Ex: Datasets of astronomy and Pharma medical knowledge.
Adaptive : Another default feature is that, once program is installed, they are known for its rigidity. There can be many changes in the behavior of a person to person ML program adapts to their inputs. Ex: Siri or Alexa. The program is trained by datasets however can adapt to almost all the users.
Types of Machine Learning:
ML is a vast domain; however, it can widely be categorized into: