in [ ] dot(bout[ ]array([ ] ][ ]]similarlythere are functions implemented for finding different products of matrices like innerouterand so on another popular matrix operation is transpose of matrix this can be easily achieved by using the function in [ ] np arange( reshape( , in [ ] out[ ]array([ ] ] ] ] ]]oftentimeswe need to find out decomposition of matrix into its constituents factors this is called matrix factorization this can be achieved by the appropriate functions popular matrix factorization method is svd factorization (covered briefly in concepts)which returns decomposition of matrix into three different matrices this can be done using linalg svd function in [ ]np linalg svd(aout[ ](array([[- ][- - ][- - ]])array( + + - ])array([[- - - - - ][- - - ] - - ][- - - ][- - - ]])linear algebra is often also used to solve system of equations using the matrix notation of system of equations and the provided function of numpywe can easily solve such system of equation consider the system of equations - - this can be represented as two matricesthe coefficient matrix ( in the exampleand the constants vector ( in the examplein [ ] np array([[ , ,- ][ ,- , ],[ , ,- ]] np array([ ,- , ] np linalg solve(abx out[ ]array( ]