Gradient Descent, Linear Regression, Machine Learning.
look at the code. We start by defining m and b with random values, then we have a for loop to iterate through derived functions and each step is controlled by a learning rate (lr). We use log and mse lists to track our progress. import numpy as np from sklearn.metrics import mean_squared_error def