array([[ ][ ][ ][ ][ ]]here we see that we were able to add up two arrays even when they were of different sizes this is achieved by the concept of broadcasting we will conclude this brief discussion on operations on arrays by demonstrating function that will return two arrays in [ ]arr np random randn( , arr out[ ]array([[- - - ] - ] ] - ] - - ]]in [ ]np modf(arr out[ ](array([[- - - ] - ] ] - ] - - ]])array([[- - - ] - ] ] - ] - - ]])the function modf will return the fractional and the integer part of the input supplied to it hence it will return two arrays of the same size we tried to give you basic idea of the operations on arrays provided by the numpy package but this list is not exhaustivefor the complete list you can refer to the reference page for ufuncs at linear algebra using numpy linear algebra is an integral part of the domain of machine learning most of the algorithms we will deal with can be concisely expressed using the operations of linear algebra numpy was initially built to provide the functions similar to matlab and hence linear algebra functions on arrays were always an important part of it in this sectionwe learn bit about performing linear algebra on ndarrays using the functions implemented in the numpy package one of the most widely used operations in linear algebra is the dot product this can be performed on two compatible (brush up on your matrices and array skills if you need to know which arrays are compatible for dot productndarrays by using the dot function in [ ] np array([[ , , ],[ , , ],[ , , ]] np array([[ , , ],[ , , ],[ , , ]]