in [ ]view matrix print( [[ [ [ ]in [ ]view dimensions print( shape( thus you can see how we can easily leverage numpy arrays to represent matrices you can think of dataset with rows and columns as matrix such that the data features or attributes are represented by columns and each row denotes data sample we will be using the same analogy later on in our analyses of courseyou can perform matrix operations like addsubtractproductsinversetransposedeterminantsand many more the following snippet shows some popular matrix operations in [ ]matrix transpose print('matrix transpose:\ ' transpose()'\ 'matrix determinant print ('matrix determinant:'np linalg det( )'\ 'matrix inverse m_inv np linalg inv(mprint ('matrix inverse:\ 'm_inv'\ 'identity matrix (result of matrix matrix_inverseiden_m np dot(mm_inviden_m np round(np abs(iden_m) print ('product of matrix and its inverse:\ 'iden_mmatrix transpose[[ [ [ ]matrix determinant- matrix inverse[[- - - - - ]product of matrix and its inverse[ ]this should give you good idea to get started with matrices and their basic operations more on this is covered in "the python machine learning ecosystem