Can we really hold the evolution of Machine ?

Updated: May 5, 2020


Machine Learning


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?


  1. 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.

  2. 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.

  3. 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: