Hello troubleshooters! here is a quick recap of day1 of machine learning where we have learnt about the introductory definition of machine learning that is ultimately nothing just a subset of artificial intelligence which coordinates well with the datasets provided in the form of input and on the top of that models are trained with the help of some algorithms collectively this whole process generates program to perform or a solution to work along, okay not trying to make it more confusing with the interchanging terms like AI , ML and model training and all.

In order to grasp the clarity we need to first try to get the ideology behind these mind boggling terms one by one

Artificial Intelligence(AI) is an umbrella discipline that covers everything related to making machines smarter.

Machine Learning (ML) is commonly used along with AI but it is a subset of AI. ML refers to an AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention are ML.

Deep Learning (DL) is a machine learning (ML) applied to large data sets.

So far we have discussed the major difference among the important terminologies hope it won't be confusing anymore. In the upcoming article of day3 we would looking forward among the various types of machine learning in little bit depth.

Read till here thanks a bunch!:)