Bhaiya told that square of a vector should be scalar in the closed form of Linear Regression video. but whenever I use numpy to make a square of vector it shows as a vector with individual elements squared ! So in what context did bhaiya told about the scalar output of vector square?
Square of a vector?
Hello @Mohit_Swain,
x*x where x is a numpy array gives you another array with element-wise multiplication. In literature, this operation is called an Hadamard product.
Here bhaiya told about scalar product, which is product of 2 vector elementwise then summing each elements giving you a scalar. To do this in numpy, you can either do the hadamard product first and sum up the resultant vector or simply compute the dot product of the vectors. Like this,
x2 = np.sum(x*x)
x2 = x.dot(x)
Happy Learning! 
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