Machine Learning, Learning, Python3, Linear Regression, Math.
np.zeros(n_features) self.bias = 0 for _ in range(self.n_iterations): # Predicted values y_pred = np.dot(X, self.weights) + self.bias # Compute gradients dw = (1/n_samples) * np.dot(X.T, (y_pred - y)) db = (1/n_samples) * np.sum(y_pred - y) # Update weights and bias self.weights -=