And the practical application of machine learning is where it is not even feasible to articulate a definite mathematical solution to the problem.A real-world problem is a candidate for the application of machine learning if – The approach of conventional programming is to feed the computer with a set of instructions for a defined set of scenarios..After that computer will utilize its computing capabilities to help human process the data faster and in an efficient fashion..Whereas in machine learning, a huge amount of data is thrown at the computer, which in turn processes all the data and comes up with something called trained model(solution)..Then this model is used to solve the unseen problems of the real world. Let us take a toy problem to demonstrate the difference..The problem takes an input number and tries to divide by 3 and 5..If the number is divisible by 3 then it prints fizz, if it is divisible by 5 then it prints out buzz and if it is divisible by both then it prints out fizzbuzz..If it is not divisible by any of the 3 or 5 then print other..Its called a Fizzbuzz game. It is extremely easy in conventional programming to feed the computer with a set of instructions because we have only 4 scenarios to be verified and print the output based on that..The python code can be written as below but you can skip reading code if you are not into coding. Supposed we already have a lot of numbers whose output is already known i.e whether it is fizz or buzz or fizzbuzz..All we need to do is now, write a machine learning code and feed (train) the available data..Then verify whether we have successfully created a model by verifying with testing with an unseen data..If model provides the output using trained model without actually calculating the result then we have achieved the purpose.We will be using Googles Tensorflow library for this purpose.. More details