introduction to machine learning and deep learning output[shape of x_train( ,shape of y_train( ,figure - toy dataset nextwe use very simple algorithm to generate modelcommonly referred to as least squares given data set of the form {( )( )(xnyn)}where rn and rthe least squares model takes the form bxwhere is vector such that is minimized herex is matrix wherein each row is an (thusx rm with being the number of examples--in our case the value of can be derived using the closed form (xtx)- xty we are glossing over lot of important details of the least squares methodbut those are secondary to the current discussion the more pertinent detail is how we transform the input variable to suitable form in our first modelwe will transform to be vector of values [ that isif it will be transformed to [ after this transformationwe can generate least squares model using the formula described previously what is happening under the hood is that we are approximating the given data with second order polynomial (degree equationand the least squares algorithm is simply curve fitting or generating the coefficients for each of [