Machine Learning Foundation Section 2, Part d: Regularization and Gradient Descent Introduction We will begin with a short tutorial on regression, polynomial features, and regularization based on a very simple, sparse data set that contains a column of x data and associated y noisy data. The data file is called X_Y_Sinusoid_Data.csv . In [ ]: import os data_path = [ r 'C:\Users\VISHAL\IBM ML\02d_regularized' ] Question 1 Import the data. Also generate approximately 100 equally spaced x data points over the range of 0 to 1. Using these points, calculate the y-data which represents the "ground truth" (the real function) from the equation: $y = sin(2\pi x)$ Plot the sparse data ( x vs y ) and the calculated ("real") data. import pandas as pd import numpy as np filepath = os . sep . join ( data_path + [ 'X_Y_Sinusoid_Data.csv' ]) data = pd . read_csv ( filepath ) X_real = np . linspace ( 0 , 1.0 , 100 ) Y